Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device

ABSTRACT

A three-dimensional data encoding method uses a first encoding scheme and a second encoding scheme different from the first encoding scheme, and includes: (i) transforming a first quantization parameter to a first scale value or (ii) transforming the first scale value to the first quantization parameter, based on a first table indicating a correspondence between values of the first quantization parameter and values of the first scale value and being shared between the first encoding scheme and the second encoding scheme; performing encoding including a first quantization process to generate encoded attribute information, the first quantization process being a process of dividing, by the first scale value, each of first coefficient values based on items of attribute information of three-dimensional points included in point cloud data; and generating a bitstream including the encoded attribute information and the first quantization parameter.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a U.S. continuation application of PCT InternationalPatent Application Number PCT/JP2020/011928 filed on Mar. 18, 2020,claiming the benefit of priority of U.S. Patent Application No.62/819,913 filed on Mar. 18, 2019, the entire contents of which arehereby incorporated by reference.

BACKGROUND 1. Technical Field

The present disclosure relates to a three-dimensional data encodingmethod, a three-dimensional data decoding method, a three-dimensionaldata encoding device, and a three-dimensional data decoding device.

2. Description of the Related Art

Devices or services utilizing three-dimensional data are expected tofind their widespread use in a wide range of fields, such as computervision that enables autonomous operations of cars or robots, mapinformation, monitoring, infrastructure inspection, and videodistribution. Three-dimensional data is obtained through various meansincluding a distance sensor such as a rangefinder, as well as a stereocamera and a combination of a plurality of monocular cameras.

Methods of representing three-dimensional data include a method known asa point cloud scheme that represents the shape of a three-dimensionalstructure by a point group in a three-dimensional space. In the pointcloud scheme, the positions and colors of a point group are stored.While point cloud is expected to be a mainstream method of representingthree-dimensional data, a massive amount of data of a point groupnecessitates compression of the amount of three-dimensional data byencoding for accumulation and transmission, as in the case of atwo-dimensional moving picture (examples include Moving Picture ExpertsGroup 4 Advanced Video Coding (MPEG-4 AVC) and High Efficiency VideoCoding (HEVC) standardized by MPEG).

Meanwhile, point cloud compression is partially supported by, forexample, an open-source library (Point Cloud Library) for pointcloud-related processing.

Furthermore, a technique for searching for and displaying a facilitylocated in the surroundings of the vehicle is known (for example, seeInternational Publication No. WO 2014/020663).

SUMMARY

There has been a demand for reducing a memory capacity to be used inthree-dimensional data encoding.

The present disclosure has an object to provide a three-dimensional dataencoding method, a three-dimensional data decoding method, athree-dimensional data encoding device, or a three-dimensional datadecoding device that is capable of reducing a memory capacity to beused.

In accordance with an aspect of the present disclosure, athree-dimensional data encoding method that uses a first encoding schemeand a second encoding scheme different from the first encoding schemeincludes: performing transformation, the transformation being one of (i)transforming a first quantization parameter to a first scale value and(ii) transforming the first scale value to the first quantizationparameter, based on a first table indicating a correspondence between aplurality of values of the first quantization parameter and a pluralityof values of the first scale value, the first table being shared betweenthe first encoding scheme and the second encoding scheme; performingencoding including a first quantization process to generate encodedattribute information, the first quantization process being a process ofdividing, by the first scale value, each of first coefficient valuesbased on items of attribute information of three-dimensional pointsincluded in point cloud data; and generating a bitstream including theencoded attribute information and the first quantization parameter.

In accordance with another aspect of the present disclosure, athree-dimensional data decoding method that uses a first decoding schemeand a second decoding scheme different from the first decoding schemeincludes: obtaining encoded attribute information and a firstquantization parameter from a bitstream, the encoded attributeinformation being generated by encoding items of attribute informationof three-dimensional points included in point cloud data; transformingthe first quantization parameter to a first scale value, based on afirst table indicating a correspondence between a plurality of values ofthe first quantization parameter and a plurality of values of the firstscale value, the first table being shared between the first decodingscheme and the second decoding scheme; performing decoding including afirst inverse quantization process to decode the items of attributeinformation, the first inverse quantization process being a process ofmultiplying, by the first scale value, each of first quantizationcoefficients based on the encoded attribute information.

The present disclosure can provide a three-dimensional data encodingmethod, a three-dimensional data decoding method, a three-dimensionaldata encoding device, or a three-dimensional data decoding device thatis capable of reducing a memory capacity to be used.

BRIEF DESCRIPTION OF DRAWINGS

These and other objects, advantages and features of the disclosure willbecome apparent from the following description thereof taken inconjunction with the accompanying drawings that illustrate a specificembodiment of the present disclosure.

FIG. 1 is a diagram showing the structure of encoded three-dimensionaldata according to Embodiment 1;

FIG. 2 is a diagram showing an example of prediction structures amongSPCs that belong to the lowermost layer in a group of spaces (GOS)according to Embodiment 1;

FIG. 3 is a diagram showing an example of prediction structures amonglayers according to Embodiment 1;

FIG. 4 is a diagram showing an example order of encoding GOSs accordingto Embodiment 1;

FIG. 5 is a diagram showing an example order of encoding GOSs accordingto Embodiment 1;

FIG. 6 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 1;

FIG. 7 is a flowchart of encoding processes according to Embodiment 1;

FIG. 8 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 1;

FIG. 9 is a flowchart of decoding processes according to Embodiment 1;

FIG. 10 is a diagram showing an example of meta information according toEmbodiment 1;

FIG. 11 is a diagram showing an example structure of a sparse world(SWLD) according to Embodiment 2;

FIG. 12 is a diagram showing example operations performed by a serverand a client according to Embodiment 2;

FIG. 13 is a diagram showing example operations performed by the serverand a client according to Embodiment 2;

FIG. 14 is a diagram showing example operations performed by the serverand the clients according to Embodiment 2;

FIG. 15 is a diagram showing example operations performed by the serverand the clients according to Embodiment 2;

FIG. 16 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 2;

FIG. 17 is a flowchart of encoding processes according to Embodiment 2;

FIG. 18 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 2;

FIG. 19 is a flowchart of decoding processes according to Embodiment 2;

FIG. 20 is a diagram showing an example structure of a world (WLD)according to Embodiment 2;

FIG. 21 is a diagram showing an example octree structure of the WLDaccording to Embodiment 2;

FIG. 22 is a diagram showing an example structure of a SWLD according toEmbodiment 2;

FIG. 23 is a diagram showing an example octree structure of the SWLDaccording to Embodiment 2;

FIG. 24 is a block diagram of a three-dimensional data creation deviceaccording to Embodiment 3;

FIG. 25 is a block diagram of a three-dimensional data transmissiondevice according to Embodiment 3;

FIG. 26 is a block diagram of a three-dimensional information processingdevice according to Embodiment 4;

FIG. 27 is a block diagram of a three-dimensional data creation deviceaccording to Embodiment 5;

FIG. 28 is a diagram showing a structure of a system according toEmbodiment 6;

FIG. 29 is a block diagram of a client device according to Embodiment 6;

FIG. 30 is a block diagram of a server according to Embodiment 6;

FIG. 31 is a flowchart of a three-dimensional data creation processperformed by the client device according to Embodiment 6;

FIG. 32 is a flowchart of a sensor information transmission processperformed by the client device according to Embodiment 6;

FIG. 33 is a flowchart of a three-dimensional data creation processperformed by the server according to Embodiment 6;

FIG. 34 is a flowchart of a three-dimensional map transmission processperformed by the server according to Embodiment 6;

FIG. 35 is a diagram showing a structure of a variation of the systemaccording to Embodiment 6;

FIG. 36 is a diagram showing a structure of the server and clientdevices according to Embodiment 6;

FIG. 37 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 7;

FIG. 38 is a diagram showing an example of a prediction residualaccording to Embodiment 7;

FIG. 39 is a diagram showing an example of a volume according toEmbodiment 7;

FIG. 40 is a diagram showing an example of an octree representation ofthe volume according to Embodiment 7;

FIG. 41 is a diagram showing an example of bit sequences of the volumeaccording to Embodiment 7;

FIG. 42 is a diagram showing an example of an octree representation of avolume according to Embodiment 7;

FIG. 43 is a diagram showing an example of the volume according toEmbodiment 7;

FIG. 44 is a diagram for describing an intra prediction processaccording to Embodiment 7;

FIG. 45 is a diagram for describing a rotation and translation processaccording to Embodiment 7;

FIG. 46 is a diagram showing an example syntax of an RT flag and RTinformation according to Embodiment 7;

FIG. 47 is a diagram for describing an inter prediction processaccording to Embodiment 7;

FIG. 48 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 7;

FIG. 49 is a flowchart of a three-dimensional data encoding processperformed by the three-dimensional data encoding device according toEmbodiment 7;

FIG. 50 is a flowchart of a three-dimensional data decoding processperformed by the three-dimensional data decoding device according toEmbodiment 7;

FIG. 51 is a diagram illustrating an example of three-dimensional pointsaccording to Embodiment 8;

FIG. 52 is a diagram illustrating an example of setting LoDs accordingto Embodiment 8;

FIG. 53 is a diagram illustrating an example of setting LoDs accordingto Embodiment 8;

FIG. 54 is a diagram illustrating an example of attribute information tobe used for predicted values according to Embodiment 8;

FIG. 55 is a diagram illustrating examples of exponential-Golomb codesaccording to Embodiment 8;

FIG. 56 is a diagram indicating a process on exponential-Golomb codesaccording to Embodiment 8;

FIG. 57 is a diagram indicating an example of a syntax in attributeheader according to Embodiment 8;

FIG. 58 is a diagram indicating an example of a syntax in attribute dataaccording to Embodiment 8;

FIG. 59 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 8;

FIG. 60 is a flowchart of an attribute information encoding processaccording to Embodiment 8;

FIG. 61 is a diagram indicating processing on exponential-Golomb codesaccording to Embodiment 8;

FIG. 62 is a diagram indicating an example of a reverse lookup tableindicating relationships between remaining codes and the values thereofaccording to Embodiment 8;

FIG. 63 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 8;

FIG. 64 is a flowchart of an attribute information decoding processaccording to Embodiment 8;

FIG. 65 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 8;

FIG. 66 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 8;

FIG. 67 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 8;

FIG. 68 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 8;

FIG. 69 is a diagram showing a first example of a table representingpredicted values calculated in prediction modes according to Embodiment9;

FIG. 70 is a diagram showing examples of attribute information items(pieces of attribute information) used as the predicted values accordingto Embodiment 9;

FIG. 71 is a diagram showing a second example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9;

FIG. 72 is a diagram showing a third example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9;

FIG. 73 is a diagram showing a fourth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9;

FIG. 74 is a diagram for describing the encoding of the attributeinformation by using a Region Adaptive Hierarchical Transform (RAHT)according to Embodiment 10;

FIG. 75 is a diagram showing an example of setting a quantization scalefor each hierarchy according to Embodiment 10;

FIG. 76 is a diagram showing an example of a first code sequence and asecond code sequence according to Embodiment 10;

FIG. 77 is a diagram showing an example of a truncated unary codeaccording to Embodiment 10;

FIG. 78 is a diagram for describing the inverse Haar conversionaccording to Embodiment 10;

FIG. 79 is a diagram showing a syntax example of the attributeinformation according to Embodiment 10;

FIG. 80 is a diagram showing an example of a coding coefficient andZeroCnt according to Embodiment 10;

FIG. 81 is a flowchart of the three-dimensional data encoding processingaccording to Embodiment 10;

FIG. 82 is a flowchart of the attribute information encoding processingaccording to Embodiment 10;

FIG. 83 is a flowchart of the coding coefficient encoding processingaccording to Embodiment 10;

FIG. 84 is a flowchart of the three-dimensional data decoding processingaccording to Embodiment 10;

FIG. 85 is a flowchart of the attribute information decoding processingaccording to Embodiment 10;

FIG. 86 is a flowchart the coding coefficient decoding processingaccording to Embodiment 10;

FIG. 87 is a block diagram of an attribute information encoder accordingto Embodiment 10;

FIG. 88 is a block diagram of an attribute information decoder accordingto Embodiment 10;

FIG. 89 is a diagram for describing a process performed by a quantizerand an inverse quantizer according to Embodiment 11;

FIG. 90 is a diagram for describing a default value and a quantizationdelta of a quantization value according to Embodiment 11;

FIG. 91 is a block diagram illustrating a configuration of a firstencoder included in a three-dimensional data encoding device accordingto Embodiment 11;

FIG. 92 is a block diagram illustrating a configuration of a divideraccording to Embodiment 11;

FIG. 93 is a block diagram illustrating a configuration of a geometryinformation encoder and an attribute information encoder according toEmbodiment 11;

FIG. 94 is a block diagram illustrating a configuration of a firstdecoder according to Embodiment 11;

FIG. 95 is a block diagram illustrating a configuration of a geometryinformation decoder and an attribute information decoder according toEmbodiment 11;

FIG. 96 is a flowchart illustrating an example of a process concerningdetermination of a quantization value in the encoding of geometryinformation or the encoding of attribute information according toEmbodiment 11;

FIG. 97 is a flowchart illustrating an example of a process of decodinggeometry information and attribute information according to Embodiment11;

FIG. 98 is a diagram for describing a first example of a method oftransmitting a quantization parameter according to Embodiment 11;

FIG. 99 is a diagram for describing a second example of the method oftransmitting a quantization parameter according to Embodiment 11;

FIG. 100 is a diagram for describing a third example of the method oftransmitting a quantization parameter according to Embodiment 11;

FIG. 101 is a flowchart of a process of encoding point cloud dataaccording to Embodiment 11;

FIG. 102 is a flowchart illustrating an example of a process ofdetermining a QP value and updating additional information according toEmbodiment 11;

FIG. 103 is a flowchart illustrating an example of a process of encodingaccording to Embodiment 11;

FIG. 104 is a flowchart illustrating a process of decoding point clouddata according to Embodiment 11;

FIG. 105 is a flowchart illustrating an example of a process ofobtaining QP values and decoding a QP value for a slice or tileaccording to Embodiment 11;

FIG. 106 is a diagram illustrating a syntax example of GPS according toEmbodiment 11;

FIG. 107 is a diagram illustrating a syntax example of APS according toEmbodiment 11;

FIG. 108 is a diagram illustrating a syntax example of a header ofgeometry information according to Embodiment 11;

FIG. 109 is a diagram illustrating a syntax example of a header ofattribute information according to Embodiment 11;

FIG. 110 is a diagram for describing another example of the method oftransmitting a quantization parameter according to Embodiment 11;

FIG. 111 is a diagram for describing another example of the method oftransmitting a quantization parameter according to Embodiment 11;

FIG. 112 is a diagram for describing a ninth example of the method oftransmitting a quantization parameter according to Embodiment 11;

FIG. 113 is a diagram for describing an example of control of a QP valueaccording to Embodiment 11;

FIG. 114 is a flowchart illustrating an example of a method ofdetermining a QP value based on the quality of an object according toEmbodiment 11;

FIG. 115 is a flowchart illustrating an example of a method ofdetermining a QP value based on a rate control according to Embodiment11;

FIG. 116 is a flowchart illustrating an encoding process according toEmbodiment 11;

FIG. 117 is a flowchart illustrating a decoding process according toEmbodiment 11;

FIG. 118 is a diagram for illustrating an example of a quantizationparameter transmission method according to Embodiment 12;

FIG. 119 is a diagram showing a first example of a syntax of APS and asyntax of a header of attribute information according to Embodiment 12;

FIG. 120 is a diagram showing a second example of the syntax of APSaccording to Embodiment 12;

FIG. 121 is a diagram showing a second example of the syntax of theheader of attribute information according to Embodiment 12;

FIG. 122 is a diagram showing a relationship between SPS, APS, and theheader of attribute information according to Embodiment 12;

FIG. 123 is a flowchart of an encoding process according to Embodiment12;

FIG. 124 is a flowchart of a decoding process according to Embodiment12;

FIG. 125 is a block diagram showing a configuration of athree-dimensional data encoding device according to Embodiment 13;

FIG. 126 is a block diagram showing a configuration of athree-dimensional data decoding device according to Embodiment 13;

FIG. 127 is a diagram showing an example of the setting of LoDsaccording to Embodiment 13;

FIG. 128 is a diagram showing an example of a hierarchical structure ofRAHT according to Embodiment 13;

FIG. 129 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 13;

FIG. 130 is a block diagram of a divider according to Embodiment 13;

FIG. 131 is a block diagram of an attribute information encoderaccording to Embodiment 13;

FIG. 132 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 13;

FIG. 133 is a block diagram of a attribute information decoder accordingto Embodiment 13;

FIG. 134 is a diagram showing an example of the setting of aquantization parameter in the tile division and the slice divisionaccording to Embodiment 13;

FIG. 135 is a diagram showing an example of the setting of aquantization parameter according to Embodiment 13;

FIG. 136 is a diagram showing an example of the setting of aquantization parameter according to Embodiment 13;

FIG. 137 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 13;

FIG. 138 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 13;

FIG. 139 is a diagram showing an example of the setting of aquantization parameter according to Embodiment 13;

FIG. 140 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 13;

FIG. 141 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 13;

FIG. 142 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 13;

FIG. 143 is a flowchart of an attribute information encoding processaccording to Embodiment 13;

FIG. 144 is a flowchart of a ΔQP determination process according toEmbodiment 13;

FIG. 145 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 13;

FIG. 146 is a flowchart of an attribute information decoding processaccording to Embodiment 13;

FIG. 147 is a block diagram of an attribute information encoderaccording to Embodiment 13;

FIG. 148 is a block diagram of an attribute information decoderaccording to Embodiment 13;

FIG. 149 is a diagram showing an example of the setting of aquantization parameter according to Embodiment 13;

FIG. 150 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 13;

FIG. 151 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 13;

FIG. 152 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 13;

FIG. 153 is a flowchart of an attribute information encoding processaccording to Embodiment 13;

FIG. 154 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 13;

FIG. 155 is a flowchart of an attribute information decoding processaccording to Embodiment 13;

FIG. 156 is a block diagram of an attribute information encoderaccording to Embodiment 13;

FIG. 157 is a block diagram of an attribute information decoderaccording to Embodiment 13;

FIG. 158 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 13;

FIG. 159 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 13;

FIG. 160 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 13;

FIG. 161 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 14;

FIG. 162 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 14;

FIG. 163 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 14;

FIG. 164 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 14;

FIG. 165 is a block diagram of an attribute information encoderaccording to Embodiment 14;

FIG. 166 is a block diagram of an attribute information decoderaccording to Embodiment 14;

FIG. 167 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 14;

FIG. 168 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 14;

FIG. 169 is a diagram showing an example of a table according toEmbodiment 14;

FIG. 170 is a block diagram of a scale value calculator according toEmbodiment 14;

FIG. 171 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 14;

FIG. 172 is a block diagram of a scale value calculator according toEmbodiment 14;

FIG. 173 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 14;

FIG. 174 is a block diagram of a scale value calculator according toEmbodiment 14;

FIG. 175 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 14;

FIG. 176 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 14; and

FIG. 177 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 14.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In accordance with an aspect of the present disclosure, athree-dimensional data encoding method that uses a first encoding schemeand a second encoding scheme different from the first encoding schemeincludes: performing transformation, the transformation being one of (i)transforming a first quantization parameter to a first scale value and(ii) transforming the first scale value to the first quantizationparameter, based on a first table indicating a correspondence between aplurality of values of the first quantization parameter and a pluralityof values of the first scale value, the first table being shared betweenthe first encoding scheme and the second encoding scheme; performingencoding including a first quantization process to generate encodedattribute information, the first quantization process being a process ofdividing, by the first scale value, each of first coefficient valuesbased on items of attribute information of three-dimensional pointsincluded in point cloud data; and generating a bitstream including theencoded attribute information and the first quantization parameter.

According to the three-dimensional data encoding method, the first tablecan be shared between the first encoding scheme and the second encodingscheme. Therefore, according to the three-dimensional data encodingmethod, the memory usage can be reduced.

For example, it is possible that the encoding including the firstquantization process includes: performing left bit shift on each of theitems of attribute information to generate items of shifted attributeinformation; and performing, on the items of shifted attributeinformation, a transformation process using items of geometryinformation of the three-dimensional points to generate the firstcoefficient values, wherein the first scale value is a value generatedby multiplying a second scale value for quantization by a coefficientcorresponding to the left bit shift, and the quantization and right bitshift are performed by dividing each of the first coefficient values bythe first scale value, the right bit shift shifting a same number ofbits as a number of bits shifted by the left bit shift.

According to the three-dimensional data encoding method, the precisioncan be improved.

For example, it is further possible that a first bit number is differentfrom a second bit number, the first bit number being a number of bitsshifted by each of the left bit shift and the right bit shift performedin the first encoding scheme, the second bit number being a number ofbits shifted by each of the left bit shift and the right bit shiftperformed in the second encoding scheme, and in the first encodingscheme, the performing of the transformation includes: (i) applying thefirst table to the first scale value to determine the first quantizationparameter; or (ii) applying the first table to the first quantizationparameter to determine the first scale value, and in the second encodingscheme, the performing of the transformation includes: (i) performingbit shift on the first scale value by a number of bits corresponding toa difference between the first bit number and the second bit number toobtain a shifted first scale value, and applying the first table to theshifted first scale value to determine the first quantization parameter;or (ii) applying the first table to the first quantization parameter todetermine a third scale value, and performing bit shift on the thirdscale value by a number of bits corresponding to the difference betweenthe first bit number and the second bit number to generate the firstscale value.

According to the three-dimensional data encoding method, a table can beshared between the first encoding scheme and the second encoding schemeeven when the number of bits shifted in the bit shift differs betweenthe first encoding scheme and the second encoding scheme. Therefore,according to the three-dimensional data encoding method, the memoryusage can be reduced.

For example, it is still further possible that the three-dimensionaldata encoding method further includes: performing transformation, thetransformation being one of (i) transforming a second quantizationparameter to a fourth scale value and (ii) transforming the fourth scalevalue to the second quantization parameter, based on a second tableindicating a correspondence between a plurality of values of the secondquantization parameter and a plurality of values of the fourth scalevalue, the second table being shared between the first encoding schemeand the second encoding scheme; and performing encoding including asecond quantization process to generate encoded geometry information,the second quantization process being a process of dividing, by thefourth scale value, each of second coefficient values based on items ofgeometry information of the three-dimensional points, wherein thebitstream further includes the encoded geometry information and thesecond quantization parameter.

According to the three-dimensional data encoding method, the secondtable can be shared between the first encoding scheme and the secondencoding scheme. Therefore, according to the three-dimensional dataencoding method, the memory usage can be reduced.

In accordance with another aspect of the present disclosure, athree-dimensional data decoding method that uses a first decoding schemeand a second decoding scheme different from the first decoding schemeincludes: obtaining encoded attribute information and a firstquantization parameter from a bitstream, the encoded attributeinformation being generated by encoding items of attribute informationof three-dimensional points included in point cloud data; transformingthe first quantization parameter to a first scale value, based on afirst table indicating a correspondence between a plurality of values ofthe first quantization parameter and a plurality of values of the firstscale value, the first table being shared between the first decodingscheme and the second decoding scheme; performing decoding including afirst inverse quantization process to decode the items of attributeinformation, the first inverse quantization process being a process ofmultiplying, by the first scale value, each of first quantizationcoefficients based on the encoded attribute information.

According to the three-dimensional data decoding method, the first tablecan be shared between the first decoding scheme and the second decodingscheme. Therefore, according to the three-dimensional data decodingmethod, the memory usage can be reduced.

For example, it is possible that the decoding including the firstinverse quantization process includes: performing the first inversequantization process to generate first coefficient values from the firstquantization coefficients; performing inverse transformation on thefirst coefficient values using items of geometry information of thethree-dimensional points to generate items of shifted attributeinformation; and performing right bit shift on each of the items ofshifted attribute information to generate the items of attributeinformation, the first scale value is a value generated by multiplying asecond scale value for inverse quantizations by a coefficientcorresponding to the right bit shift, and left bit shift and the inversequantization are performed by multiplying each of the first quantizationcoefficients by the first scale value, the left bit shift shifting asame number of bits as a number of bits shifted by the right bit shift.

According to the three-dimensional data decoding method, the precisioncan be improved.

For example, it is further possible that a first bit number is differentfrom a second bit number, the first bit number being a number of bitsshifted by each of the right bit shift and the left bit shift performedin the first decoding scheme, the second bit number being a number ofbits shifted by each of the right bit shift and the left bit shiftperformed in the second decoding scheme, and in the first decodingscheme, the transforming of the first quantization parameter to thefirst scale value includes applying the first table to the firstquantization parameter to determine the first scale value, and in thesecond decoding scheme, the transforming of the first quantizationparameter to the first scale value includes applying the first table tothe first quantization parameter to determine a third scale value, andperforming bit shift on the third scale value by a number of bitscorresponding to a difference between the first bit number and thesecond bit number.

According to the three-dimensional data decoding method, a table can beshared between the first decoding scheme and the second decoding schemeeven when the number of bits shifted in the bit shift differs betweenthe first decoding scheme and the second decoding scheme. Therefore,according to the three-dimensional data decoding method, the memoryusage can be reduced.

For example, it is still further possible that the three-dimensionaldata decoding method further includes: obtaining encoded geometryinformation and a second quantization parameter from the bitstream, theencoded geometry information being generated by encoding items ofgeometry information of the three-dimensional points; transforming thesecond quantization parameter to a fourth scale value, based on a secondtable indicating a correspondence between a plurality of values of thesecond quantization parameter and a plurality of values of the fourthscale value, the second table being shared between the first decodingscheme and the second decoding scheme; performing decoding including asecond inverse quantization process to obtain the items of geometryinformation, the second inverse quantization process being a process ofmultiplying, by the fourth scale value, each of second quantizationcoefficients based on the encoded geometry information.

According to the three-dimensional data decoding method, the secondtable can be shared between the first decoding scheme and the seconddecoding scheme. Therefore, according to the three-dimensional datadecoding method, the memory usage can be reduced.

For example, it is still further possible that when the firstquantization parameter is smaller than 4, the first quantizationparameter is assumed to be 4.

According to the three-dimensional data decoding method, the decodingcan be properly performed.

In accordance with still another aspect of the present disclosure, athree-dimensional data encoding device that uses a first encoding schemeand a second encoding scheme different from the first encoding schemeincludes: a processor; and memory, wherein using the memory, theprocessor: performs transformation, the transformation being one of (i)transforming a first quantization parameter to a first scale value and(ii) transforming the first scale value to the first quantizationparameter, based on a first table indicating a correspondence between aplurality of values of the first quantization parameter and a pluralityof values of the first scale value, the first table being shared betweenthe first encoding scheme and the second encoding scheme; performsencoding including a first quantization process to generate encodedattribute information, the first quantization process being a process ofdividing, by the first scale value, each of first coefficient valuesbased on items of attribute information of three-dimensional pointsincluded in point cloud data; and generates a bitstream including theencoded attribute information and the first quantization parameter.

With this, the three-dimensional data encoding device can use a commonfirst table for the first encoding scheme and the second encodingscheme. Therefore, the three-dimensional data encoding device can reducethe memory usage.

In accordance with still another aspect of the present disclosure, athree-dimensional data decoding device that uses a first decoding schemeand a second decoding scheme different from the first decoding schemeincludes: a processor; and memory, wherein using the memory, theprocessor: obtains encoded attribute information and a firstquantization parameter from a bitstream, the encoded attributeinformation being generated by encoding items of attribute informationof three-dimensional points included in point cloud data; transforms thefirst quantization parameter to a first scale value, based on a firsttable indicating a correspondence between a plurality of values of thefirst quantization parameter and a plurality of values of the firstscale value, the first table being shared between the first decodingscheme and the second decoding scheme; performs decoding including afirst inverse quantization process to decode the items of attributeinformation, the first inverse quantization process being a process ofmultiplying, by the first scale value, each of first quantizationcoefficients based on the encoded attribute information.

With this, the three-dimensional data decoding device can use a commonfirst table for the first decoding scheme and the second decodingscheme. Therefore, the three-dimensional data decoding device can reducethe memory usage.

It is to be noted that these general or specific aspects may beimplemented as a system, a method, an integrated circuit, a computerprogram, or a computer-readable recording medium such as a Compact DiscRead Only Memory (CD-ROM), or may be implemented as any combination of asystem, a method, an integrated circuit, a computer program, and arecording medium.

The following describes embodiments with reference to the drawings. Itis to be noted that the following embodiments indicate exemplaryembodiments of the present disclosure. The numerical values, shapes,materials, constituent elements, the arrangement and connection of theconstituent elements, steps, the processing order of the steps, etc.indicated in the following embodiments are mere examples, and thus arenot intended to limit the present disclosure. Of the constituentelements described in the following embodiments, constituent elementsnot recited in any one of the independent claims that indicate thebroadest concepts will be described as optional constituent elements.

Embodiment 1

First, the data structure of encoded three-dimensional data (hereinafteralso referred to as encoded data) according to the present embodimentwill be described. FIG. 1 is a diagram showing the structure of encodedthree-dimensional data according to the present embodiment.

In the present embodiment, a three-dimensional space is divided intospaces (SPCs), which correspond to pictures in moving picture encoding,and the three-dimensional data is encoded on a SPC-by-SPC basis. EachSPC is further divided into volumes (VLMs), which correspond tomacroblocks, etc. in moving picture encoding, and predictions andtransforms are performed on a VLM-by-VLM basis. Each volume includes aplurality of voxels (VXLs), each being a minimum unit in which positioncoordinates are associated. Note that prediction is a process ofgenerating predictive three-dimensional data analogous to a currentprocessing unit by referring to another processing unit, and encoding adifferential between the predictive three-dimensional data and thecurrent processing unit, as in the case of predictions performed ontwo-dimensional images. Such prediction includes not only spatialprediction in which another prediction unit corresponding to the sametime is referred to, but also temporal prediction in which a predictionunit corresponding to a different time is referred to.

When encoding a three-dimensional space represented by point group datasuch as a point cloud, for example, the three-dimensional data encodingdevice (hereinafter also referred to as the encoding device) encodes thepoints in the point group or points included in the respective voxels ina collective manner, in accordance with a voxel size. Finer voxelsenable a highly-precise representation of the three-dimensional shape ofa point group, while larger voxels enable a rough representation of thethree-dimensional shape of a point group.

Note that the following describes the case where three-dimensional datais a point cloud, but three-dimensional data is not limited to a pointcloud, and thus three-dimensional data of any format may be employed.

Also note that voxels with a hierarchical structure may be used. In sucha case, when the hierarchy includes n levels, whether a sampling pointis included in the n−1th level or lower levels (levels below the n-thlevel) may be sequentially indicated. For example, when only the n-thlevel is decoded, and the n−1th level or lower levels include a samplingpoint, the n-th level can be decoded on the assumption that a samplingpoint is included at the center of a voxel in the n-th level.

Also, the encoding device obtains point group data, using, for example,a distance sensor, a stereo camera, a monocular camera, a gyroscopesensor, or an inertial sensor.

As in the case of moving picture encoding, each SPC is classified intoone of at least the three prediction structures that include: intra SPC(I-SPC), which is individually decodable; predictive SPC (P-SPC) capableof only a unidirectional reference; and bidirectional SPC (B-SPC)capable of bidirectional references. Each SPC includes two types of timeinformation: decoding time and display time.

Furthermore, as shown in FIG. 1 , a processing unit that includes aplurality of SPCs is a group of spaces (GOS), which is a random accessunit. Also, a processing unit that includes a plurality of GOSs is aworld (WLD).

The spatial region occupied by each world is associated with an absoluteposition on earth, by use of, for example, GPS, or latitude andlongitude information. Such position information is stored asmeta-information. Note that meta-information may be included in encodeddata, or may be transmitted separately from the encoded data.

Also, inside a GOS, all SPCs may be three-dimensionally adjacent to oneanother, or there may be a SPC that is not three-dimensionally adjacentto another SPC.

Note that the following also describes processes such as encoding,decoding, and reference to be performed on three-dimensional dataincluded in processing units such as GOS, SPC, and VLM, simply asperforming encoding/to encode, decoding/to decode, referring to, etc. ona processing unit. Also note that three-dimensional data included in aprocessing unit includes, for example, at least one pair of a spatialposition such as three-dimensional coordinates and an attribute valuesuch as color information.

Next, the prediction structures among SPCs in a GOS will be described. Aplurality of SPCs in the same GOS or a plurality of VLMs in the same SPCoccupy mutually different spaces, while having the same time information(the decoding time and the display time).

A SPC in a GOS that comes first in the decoding order is an I-SPC. GOSscome in two types: closed GOS and open GOS. A closed GOS is a GOS inwhich all SPCs in the GOS are decodable when decoding starts from thefirst I-SPC. Meanwhile, an open GOS is a GOS in which a different GOS isreferred to in one or more SPCs preceding the first I-SPC in the GOS inthe display time, and thus cannot be singly decoded.

Note that in the case of encoded data of map information, for example, aWLD is sometimes decoded in the backward direction, which is opposite tothe encoding order, and thus backward reproduction is difficult whenGOSs are interdependent. In such a case, a closed GOS is basically used.

Each GOS has a layer structure in height direction, and SPCs aresequentially encoded or decoded from SPCs in the bottom layer.

FIG. 2 is a diagram showing an example of prediction structures amongSPCs that belong to the lowermost layer in a GOS. FIG. 3 is a diagramshowing an example of prediction structures among layers.

A GOS includes at least one I-SPC. Of the objects in a three-dimensionalspace, such as a person, an animal, a car, a bicycle, a signal, and abuilding serving as a landmark, a small-sized object is especiallyeffective when encoded as an I-SPC. When decoding a GOS at a lowthroughput or at a high speed, for example, the three-dimensional datadecoding device (hereinafter also referred to as the decoding device)decodes only I-SPC(s) in the GOS.

The encoding device may also change the encoding interval or theappearance frequency of I-SPCs, depending on the degree of sparsenessand denseness of the objects in a WLD.

In the structure shown in FIG. 3 , the encoding device or the decodingdevice encodes or decodes a plurality of layers sequentially from thebottom layer (layer 1). This increases the priority of data on theground and its vicinity, which involve a larger amount of information,when, for example, a self-driving car is concerned.

Regarding encoded data used for a drone, for example, encoding ordecoding may be performed sequentially from SPCs in the top layer in aGOS in height direction.

The encoding device or the decoding device may also encode or decode aplurality of layers in a manner that the decoding device can have arough grasp of a GOS first, and then the resolution is graduallyincreased. The encoding device or the decoding device may performencoding or decoding in the order of layers 3, 8, 1, 9 . . . , forexample.

Next, the handling of static objects and dynamic objects will bedescribed.

A three-dimensional space includes scenes or still objects such as abuilding and a road (hereinafter collectively referred to as staticobjects), and objects with motion such as a car and a person(hereinafter collectively referred to as dynamic objects). Objectdetection is separately performed by, for example, extracting keypointsfrom point cloud data, or from video of a camera such as a stereocamera. In this description, an example method of encoding a dynamicobject will be described.

A first method is a method in which a static object and a dynamic objectare encoded without distinction. A second method is a method in which adistinction is made between a static object and a dynamic object on thebasis of identification information.

For example, a GOS is used as an identification unit. In such a case, adistinction is made between a GOS that includes SPCs constituting astatic object and a GOS that includes SPCs constituting a dynamicobject, on the basis of identification information stored in the encodeddata or stored separately from the encoded data.

Alternatively, a SPC may be used as an identification unit. In such acase, a distinction is made between a SPC that includes VLMsconstituting a static object and a SPC that includes VLMs constituting adynamic object, on the basis of the identification information thusdescribed.

Alternatively, a VLM or a VXL may be used as an identification unit. Insuch a case, a distinction is made between a VLM or a VXL that includesa static object and a VLM or a VXL that includes a dynamic object, onthe basis of the identification information thus described.

The encoding device may also encode a dynamic object as at least one VLMor SPC, and may encode a VLM or a SPC including a static object and aSPC including a dynamic object as mutually different GOSs. When the GOSsize is variable depending on the size of a dynamic object, the encodingdevice separately stores the GOS size as meta-information.

The encoding device may also encode a static object and a dynamic objectseparately from each other, and may superimpose the dynamic object ontoa world constituted by static objects. In such a case, the dynamicobject is constituted by at least one SPC, and each SPC is associatedwith at least one SPC constituting the static object onto which the eachSPC is to be superimposed. Note that a dynamic object may be representednot by SPC(s) but by at least one VLM or VXL.

The encoding device may also encode a static object and a dynamic objectas mutually different streams.

The encoding device may also generate a GOS that includes at least oneSPC constituting a dynamic object. The encoding device may further setthe size of a GOS including a dynamic object (GOS_M) and the size of aGOS including a static object corresponding to the spatial region ofGOS_M at the same size (such that the same spatial region is occupied).This enables superimposition to be performed on a GOS-by-GOS basis.

SPC(s) included in another encoded GOS may be referred to in a P-SPC ora B-SPC constituting a dynamic object. In the case where the position ofa dynamic object temporally changes, and the same dynamic object isencoded as an object in a GOS corresponding to a different time,referring to SPC(s) across GOSs is effective in terms of compressionrate.

The first method and the second method may be selected in accordancewith the intended use of encoded data. When encoded three-dimensionaldata is used as a map, for example, a dynamic object is desired to beseparated, and thus the encoding device uses the second method.Meanwhile, the encoding device uses the first method when the separationof a dynamic object is not required such as in the case wherethree-dimensional data of an event such as a concert and a sports eventis encoded.

The decoding time and the display time of a GOS or a SPC are storable inencoded data or as meta-information. All static objects may have thesame time information. In such a case, the decoding device may determinethe actual decoding time and display time. Alternatively, a differentvalue may be assigned to each GOS or SPC as the decoding time, and thesame value may be assigned as the display time. Furthermore, as in thecase of the decoder model in moving picture encoding such asHypothetical Reference Decoder (HRD) compliant with HEVC, a model may beemployed that ensures that a decoder can perform decoding without failby having a buffer of a predetermined size and by reading a bitstream ata predetermined bit rate in accordance with the decoding times.

Next, the topology of GOSs in a world will be described. The coordinatesof the three-dimensional space in a world are represented by the threecoordinate axes (x axis, y axis, and z axis) that are orthogonal to oneanother. A predetermined rule set for the encoding order of GOSs enablesencoding to be performed such that spatially adjacent GOSs arecontiguous in the encoded data. In an example shown in FIG. 4 , forexample, GOSs in the x and z planes are successively encoded. After thecompletion of encoding all GOSs in certain x and z planes, the value ofthe y axis is updated. Stated differently, the world expands in the yaxis direction as the encoding progresses. The GOS index numbers are setin accordance with the encoding order.

Here, the three-dimensional spaces in the respective worlds arepreviously associated one-to-one with absolute geographical coordinatessuch as GPS coordinates or latitude/longitude coordinates.Alternatively, each three-dimensional space may be represented as aposition relative to a previously set reference position. The directionsof the x axis, the y axis, and the z axis in the three-dimensional spaceare represented by directional vectors that are determined on the basisof the latitudes and the longitudes, etc. Such directional vectors arestored together with the encoded data as meta-information.

GOSs have a fixed size, and the encoding device stores such size asmeta-information. The GOS size may be changed depending on, for example,whether it is an urban area or not, or whether it is inside or outsideof a room. Stated differently, the GOS size may be changed in accordancewith the amount or the attributes of objects with information values.Alternatively, in the same world, the encoding device may adaptivelychange the GOS size or the interval between I-SPCs in GOSs in accordancewith the object density, etc. For example, the encoding device sets theGOS size to smaller and the interval between I-SPCs in GOSs to shorter,as the object density is higher.

In an example shown in FIG. 5 , to enable random access with a finergranularity, a GOS with a high object density is partitioned into theregions of the third to tenth GOSs. Note that the seventh to tenth GOSsare located behind the third to sixth GOSs.

Next, the structure and the operation flow of the three-dimensional dataencoding device according to the present embodiment will be described.FIG. 6 is a block diagram of three-dimensional data encoding device 100according to the present embodiment. FIG. 7 is a flowchart of an exampleoperation performed by three-dimensional data encoding device 100.

Three-dimensional data encoding device 100 shown in FIG. 6 encodesthree-dimensional data 111, thereby generating encoded three-dimensionaldata 112. Such three-dimensional data encoding device 100 includesobtainer 101, encoding region determiner 102, divider 103, and encoder104.

As shown in FIG. 7 , first, obtainer 101 obtains three-dimensional data111, which is point group data (S101).

Next, encoding region determiner 102 determines a current region forencoding from among spatial regions corresponding to the obtained pointgroup data (S102). For example, in accordance with the position of auser or a vehicle, encoding region determiner 102 determines, as thecurrent region, a spatial region around such position.

Next, divider 103 divides the point group data included in the currentregion into processing units. The processing units here means units suchas GOSs and SPCs described above. The current region here correspondsto, for example, a world described above. More specifically, divider 103divides the point group data into processing units on the basis of apredetermined GOS size, or the presence/absence/size of a dynamic object(S103). Divider 103 further determines the starting position of the SPCthat comes first in the encoding order in each GOS.

Next, encoder 104 sequentially encodes a plurality of SPCs in each GOS,thereby generating encoded three-dimensional data 112 (S104).

Note that although an example is described here in which the currentregion is divided into GOSs and SPCs, after which each GOS is encoded,the processing steps are not limited to this order. For example, stepsmay be employed in which the structure of a single GOS is determined,which is followed by the encoding of such GOS, and then the structure ofthe subsequent GOS is determined.

As thus described, three-dimensional data encoding device 100 encodesthree-dimensional data 111, thereby generating encoded three-dimensionaldata 112. More specifically, three-dimensional data encoding device 100divides three-dimensional data into first processing units (GOSs), eachbeing a random access unit and being associated with three-dimensionalcoordinates, divides each of the first processing units (GOSs) intosecond processing units (SPCs), and divides each of the secondprocessing units (SPCs) into third processing units (VLMs). Each of thethird processing units (VLMs) includes at least one voxel (VXL), whichis the minimum unit in which position information is associated.

Next, three-dimensional data encoding device 100 encodes each of thefirst processing units (GOSs), thereby generating encodedthree-dimensional data 112. More specifically, three-dimensional dataencoding device 100 encodes each of the second processing units (SPCs)in each of the first processing units (GOSs). Three-dimensional dataencoding device 100 further encodes each of the third processing units(VLMs) in each of the second processing units (SPCs).

When a current first processing unit (GOS) is a closed GOS, for example,three-dimensional data encoding device 100 encodes a current secondprocessing unit (SPC) included in such current first processing unit(GOS) by referring to another second processing unit (SPC) included inthe current first processing unit (GOS). Stated differently,three-dimensional data encoding device 100 refers to no secondprocessing unit (SPC) included in a first processing unit (GOS) that isdifferent from the current first processing unit (GOS).

Meanwhile, when a current first processing unit (GOS) is an open GOS,three-dimensional data encoding device 100 encodes a current secondprocessing unit (SPC) included in such current first processing unit(GOS) by referring to another second processing unit (SPC) included inthe current first processing unit (GOS) or a second processing unit(SPC) included in a first processing unit (GOS) that is different fromthe current first processing unit (GOS).

Also, three-dimensional data encoding device 100 selects, as the type ofa current second processing unit (SPC), one of the following: a firsttype (I-SPC) in which another second processing unit (SPC) is notreferred to; a second type (P-SPC) in which another single secondprocessing unit (SPC) is referred to; and a third type in which othertwo second processing units (SPC) are referred to. Three-dimensionaldata encoding device 100 encodes the current second processing unit(SPC) in accordance with the selected type.

Next, the structure and the operation flow of the three-dimensional datadecoding device according to the present embodiment will be described.FIG. 8 is a block diagram of three-dimensional data decoding device 200according to the present embodiment. FIG. 9 is a flowchart of an exampleoperation performed by three-dimensional data decoding device 200.

Three-dimensional data decoding device 200 shown in FIG. 8 decodesencoded three-dimensional data 211, thereby generating decodedthree-dimensional data 212. Encoded three-dimensional data 211 here is,for example, encoded three-dimensional data 112 generated bythree-dimensional data encoding device 100. Such three-dimensional datadecoding device 200 includes obtainer 201, decoding start GOS determiner202, decoding SPC determiner 203, and decoder 204.

First, obtainer 201 obtains encoded three-dimensional data 211 (S201).Next, decoding start GOS determiner 202 determines a current GOS fordecoding (S202). More specifically, decoding start GOS determiner 202refers to meta-information stored in encoded three-dimensional data 211or stored separately from the encoded three-dimensional data todetermine, as the current GOS, a GOS that includes a SPC correspondingto the spatial position, the object, or the time from which decoding isto start.

Next, decoding SPC determiner 203 determines the type(s) (I, P, and/orB) of SPCs to be decoded in the GOS (S203). For example, decoding SPCdeterminer 203 determines whether to (1) decode only I-SPC(s), (2) todecode I-SPC(s) and P-SPCs, or (3) to decode SPCs of all types. Notethat the present step may not be performed, when the type(s) of SPCs tobe decoded are previously determined such as when all SPCs arepreviously determined to be decoded.

Next, decoder 204 obtains an address location within encodedthree-dimensional data 211 from which a SPC that comes first in the GOSin the decoding order (the same as the encoding order) starts. Decoder204 obtains the encoded data of the first SPC from the address location,and sequentially decodes the SPCs from such first SPC (S204). Note thatthe address location is stored in the meta-information, etc.

Three-dimensional data decoding device 200 decodes decodedthree-dimensional data 212 as thus described. More specifically,three-dimensional data decoding device 200 decodes each encodedthree-dimensional data 211 of the first processing units (GOSs), eachbeing a random access unit and being associated with three-dimensionalcoordinates, thereby generating decoded three-dimensional data 212 ofthe first processing units (GOSs). Even more specifically,three-dimensional data decoding device 200 decodes each of the secondprocessing units (SPCs) in each of the first processing units (GOSs).Three-dimensional data decoding device 200 further decodes each of thethird processing units (VLMs) in each of the second processing units(SPCs).

The following describes meta-information for random access. Suchmeta-information is generated by three-dimensional data encoding device100, and included in encoded three-dimensional data 112 (211).

In the conventional random access for a two-dimensional moving picture,decoding starts from the first frame in a random access unit that isclose to a specified time. Meanwhile, in addition to times, randomaccess to spaces (coordinates, objects, etc.) is assumed to be performedin a world.

To enable random access to at least three elements of coordinates,objects, and times, tables are prepared that associate the respectiveelements with the GOS index numbers. Furthermore, the GOS index numbersare associated with the addresses of the respective first I-SPCs in theGOSs. FIG. 10 is a diagram showing example tables included in themeta-information. Note that not all the tables shown in FIG. 10 arerequired to be used, and thus at least one of the tables is used.

The following describes an example in which random access is performedfrom coordinates as a starting point. To access the coordinates (x2, y2,and z2), the coordinates-GOS table is first referred to, which indicatesthat the point corresponding to the coordinates (x2, y2, and z2) isincluded in the second GOS. Next, the GOS-address table is referred to,which indicates that the address of the first I-SPC in the second GOS isaddr(2). As such, decoder 204 obtains data from this address to startdecoding.

Note that the addresses may either be logical addresses or physicaladdresses of an HDD or a memory. Alternatively, information thatidentifies file segments may be used instead of addresses. File segmentsare, for example, units obtained by segmenting at least one GOS, etc.

When an object spans across a plurality of GOSs, the object-GOS tablemay show a plurality of GOSs to which such object belongs. When suchplurality of GOSs are closed GOSs, the encoding device and the decodingdevice can perform encoding or decoding in parallel. Meanwhile, whensuch plurality of GOSs are open GOSs, a higher compression efficiency isachieved by the plurality of GOSs referring to each other.

Example objects include a person, an animal, a car, a bicycle, a signal,and a building serving as a landmark. For example, three-dimensionaldata encoding device 100 extracts keypoints specific to an object from athree-dimensional point cloud, etc., when encoding a world, and detectsthe object on the basis of such keypoints to set the detected object asa random access point.

As thus described, three-dimensional data encoding device 100 generatesfirst information indicating a plurality of first processing units(GOSs) and the three-dimensional coordinates associated with therespective first processing units (GOSs). Encoded three-dimensional data112 (211) includes such first information. The first information furtherindicates at least one of objects, times, and data storage locationsthat are associated with the respective first processing units (GOSs).

Three-dimensional data decoding device 200 obtains the first informationfrom encoded three-dimensional data 211. Using such first information,three-dimensional data decoding device 200 identifies encodedthree-dimensional data 211 of the first processing unit that correspondsto the specified three-dimensional coordinates, object, or time, anddecodes encoded three-dimensional data 211.

The following describes an example of other meta-information. Inaddition to the meta-information for random access, three-dimensionaldata encoding device 100 may also generate and store meta-information asdescribed below, and three-dimensional data decoding device 200 may usesuch meta-information at the time of decoding.

When three-dimensional data is used as map information, for example, aprofile is defined in accordance with the intended use, and informationindicating such profile may be included in meta-information. Forexample, a profile is defined for an urban or a suburban area, or for aflying object, and the maximum or minimum size, etc. of a world, a SPCor a VLM, etc. is defined in each profile. For example, more detailedinformation is required for an urban area than for a suburban area, andthus the minimum VLM size is set to small.

The meta-information may include tag values indicating object types.Each of such tag values is associated with VLMs, SPCs, or GOSs thatconstitute an object. For example, a tag value may be set for eachobject type in a manner, for example, that the tag value “0” indicates“person,” the tag value “1” indicates “car,” and the tag value “2”indicates “signal”. Alternatively, when an object type is hard to judge,or such judgment is not required, a tag value may be used that indicatesthe size or the attribute indicating, for example, whether an object isa dynamic object or a static object.

The meta-information may also include information indicating a range ofthe spatial region occupied by a world.

The meta-information may also store the SPC or VXL size as headerinformation common to the whole stream of the encoded data or to aplurality of SPCs, such as SPCs in a GOS.

The meta-information may also include identification information on adistance sensor or a camera that has been used to generate a pointcloud, or information indicating the positional accuracy of a pointgroup in the point cloud.

The meta-information may also include information indicating whether aworld is made only of static objects or includes a dynamic object.

The following describes variations of the present embodiment.

The encoding device or the decoding device may encode or decode two ormore mutually different SPCs or GOSs in parallel. GOSs to be encoded ordecoded in parallel can be determined on the basis of meta-information,etc. indicating the spatial positions of the GOSs.

When three-dimensional data is used as a spatial map for use by a car ora flying object, etc. in traveling, or for creation of such a spatialmap, for example, the encoding device or the decoding device may encodeor decode GOSs or SPCs included in a space that is identified on thebasis of GPS information, the route information, the zoom magnification,etc.

The decoding device may also start decoding sequentially from a spacethat is close to the self-location or the traveling route. The encodingdevice or the decoding device may give a lower priority to a spacedistant from the self-location or the traveling route than the priorityof a nearby space to encode or decode such distant place. To “give alower priority” means here, for example, to lower the priority in theprocessing sequence, to decrease the resolution (to apply decimation inthe processing), or to lower the image quality (to increase the encodingefficiency by, for example, setting the quantization step to larger).

When decoding encoded data that is hierarchically encoded in a space,the decoding device may decode only the bottom layer in the hierarchy.

The decoding device may also start decoding preferentially from thebottom layer of the hierarchy in accordance with the zoom magnificationor the intended use of the map.

For self-location estimation or object recognition, etc. involved in theself-driving of a car or a robot, the encoding device or the decodingdevice may encode or decode regions at a lower resolution, except for aregion that is lower than or at a specified height from the ground (theregion to be recognized).

The encoding device may also encode point clouds representing thespatial shapes of a room interior and a room exterior separately. Forexample, the separation of a GOS representing a room interior (interiorGOS) and a GOS representing a room exterior (exterior GOS) enables thedecoding device to select a GOS to be decoded in accordance with aviewpoint location, when using the encoded data.

The encoding device may also encode an interior GOS and an exterior GOShaving close coordinates so that such GOSs come adjacent to each otherin an encoded stream. For example, the encoding device associates theidentifiers of such GOSs with each other, and stores informationindicating the associated identifiers into the meta-information that isstored in the encoded stream or stored separately. This enables thedecoding device to refer to the information in the meta-information toidentify an interior GOS and an exterior GOS having close coordinates.

The encoding device may also change the GOS size or the SPC sizedepending on whether a GOS is an interior GOS or an exterior GOS. Forexample, the encoding device sets the size of an interior GOS to smallerthan the size of an exterior GOS. The encoding device may also changethe accuracy of extracting keypoints from a point cloud, or the accuracyof detecting objects, for example, depending on whether a GOS is aninterior GOS or an exterior GOS.

The encoding device may also add, to encoded data, information by whichthe decoding device displays objects with a distinction between adynamic object and a static object. This enables the decoding device todisplay a dynamic object together with, for example, a red box orletters for explanation. Note that the decoding device may display onlya red box or letters for explanation, instead of a dynamic object. Thedecoding device may also display more particular object types. Forexample, a red box may be used for a car, and a yellow box may be usedfor a person.

The encoding device or the decoding device may also determine whether toencode or decode a dynamic object and a static object as a different SPCor GOS, in accordance with, for example, the appearance frequency ofdynamic objects or a ratio between static objects and dynamic objects.For example, when the appearance frequency or the ratio of dynamicobjects exceeds a threshold, a SPC or a GOS including a mixture of adynamic object and a static object is accepted, while when theappearance frequency or the ratio of dynamic objects is below athreshold, a SPC or GOS including a mixture of a dynamic object and astatic object is unaccepted.

When detecting a dynamic object not from a point cloud but fromtwo-dimensional image information of a camera, the encoding device mayseparately obtain information for identifying a detection result (box orletters) and the object position, and encode these items of informationas part of the encoded three-dimensional data. In such a case, thedecoding device superimposes auxiliary information (box or letters)indicating the dynamic object onto a resultant of decoding a staticobject to display it.

The encoding device may also change the sparseness and denseness of VXLsor VLMs in a SPC in accordance with the degree of complexity of theshape of a static object. For example, the encoding device sets VXLs orVLMs at a higher density as the shape of a static object is morecomplex. The encoding device may further determine a quantization step,etc. for quantizing spatial positions or color information in accordancewith the sparseness and denseness of VXLs or VLMs. For example, theencoding device sets the quantization step to smaller as the density ofVXLs or VLMs is higher.

As described above, the encoding device or the decoding device accordingto the present embodiment encodes or decodes a space on a SPC-by-SPCbasis that includes coordinate information.

Furthermore, the encoding device and the decoding device performencoding or decoding on a volume-by-volume basis in a SPC. Each volumeincludes a voxel, which is the minimum unit in which positioninformation is associated.

Also, using a table that associates the respective elements of spatialinformation including coordinates, objects, and times with GOSs or usinga table that associates these elements with each other, the encodingdevice and the decoding device associate any ones of the elements witheach other to perform encoding or decoding. The decoding device uses thevalues of the selected elements to determine the coordinates, andidentifies a volume, a voxel, or a SPC from such coordinates to decode aSPC including such volume or voxel, or the identified SPC.

Furthermore, the encoding device determines a volume, a voxel, or a SPCthat is selectable in accordance with the elements, through extractionof keypoints and object recognition, and encodes the determined volume,voxel, or SPC, as a volume, a voxel, or a SPC to which random access ispossible.

SPCs are classified into three types: I-SPC that is singly encodable ordecodable; P-SPC that is encoded or decoded by referring to any one ofthe processed SPCs; and B-SPC that is encoded or decoded by referring toany two of the processed SPCs.

At least one volume corresponds to a static object or a dynamic object.A SPC including a static object and a SPC including a dynamic object areencoded or decoded as mutually different GOSs. Stated differently, a SPCincluding a static object and a SPC including a dynamic object areassigned to different GOSs.

Dynamic objects are encoded or decoded on an object-by-object basis, andare associated with at least one SPC including a static object. Stateddifferently, a plurality of dynamic objects are individually encoded,and the obtained encoded data of the dynamic objects is associated witha SPC including a static object.

The encoding device and the decoding device give an increased priorityto I-SPC(s) in a GOS to perform encoding or decoding. For example, theencoding device performs encoding in a manner that prevents thedegradation of I-SPCs (in a manner that enables the originalthree-dimensional data to be reproduced with a higher fidelity afterdecoded). The decoding device decodes, for example, only I-SPCs.

The encoding device may change the frequency of using I-SPCs dependingon the sparseness and denseness or the number (amount) of the objects ina world to perform encoding. Stated differently, the encoding devicechanges the frequency of selecting I-SPCs depending on the number or thesparseness and denseness of the objects included in thethree-dimensional data. For example, the encoding device uses I-SPCs ata higher frequency as the density of the objects in a world is higher.

The encoding device also sets random access points on a GOS-by-GOSbasis, and stores information indicating the spatial regionscorresponding to the GOSs into the header information.

The encoding device uses, for example, a default value as the spatialsize of a GOS. Note that the encoding device may change the GOS sizedepending on the number (amount) or the sparseness and denseness ofobjects or dynamic objects. For example, the encoding device sets thespatial size of a GOS to smaller as the density of objects or dynamicobjects is higher or the number of objects or dynamic objects isgreater.

Also, each SPC or volume includes a keypoint group that is derived byuse of information obtained by a sensor such as a depth sensor, agyroscope sensor, or a camera sensor. The coordinates of the keypointsare set at the central positions of the respective voxels. Furthermore,finer voxels enable highly accurate position information.

The keypoint group is derived by use of a plurality of pictures. Aplurality of pictures include at least two types of time information:the actual time information and the same time information common to aplurality of pictures that are associated with SPCs (for example, theencoding time used for rate control, etc.).

Also, encoding or decoding is performed on a GOS-by-GOS basis thatincludes at least one SPC.

The encoding device and the decoding device predict P-SPCs or B-SPCs ina current GOS by referring to SPCs in a processed GOS.

Alternatively, the encoding device and the decoding device predictP-SPCs or B-SPCs in a current GOS, using the processed SPCs in thecurrent GOS, without referring to a different GOS.

Furthermore, the encoding device and the decoding device transmit orreceive an encoded stream on a world-by-world basis that includes atleast one GOS.

Also, a GOS has a layer structure in one direction at least in a world,and the encoding device and the decoding device start encoding ordecoding from the bottom layer. For example, a random accessible GOSbelongs to the lowermost layer. A GOS that belongs to the same layer ora lower layer is referred to in a GOS that belongs to an upper layer.Stated differently, a GOS is spatially divided in a predetermineddirection in advance to have a plurality of layers, each including atleast one SPC. The encoding device and the decoding device encode ordecode each SPC by referring to a SPC included in the same layer as theeach SPC or a SPC included in a layer lower than that of the each SPC.

Also, the encoding device and the decoding device successively encode ordecode GOSs on a world-by-world basis that includes such GOSs. In sodoing, the encoding device and the decoding device write or read outinformation indicating the order (direction) of encoding or decoding asmetadata. Stated differently, the encoded data includes informationindicating the order of encoding a plurality of GOSs.

The encoding device and the decoding device also encode or decodemutually different two or more SPCs or GOSs in parallel.

Furthermore, the encoding device and the decoding device encode ordecode the spatial information (coordinates, size, etc.) on a SPC or aGOS.

The encoding device and the decoding device encode or decode SPCs orGOSs included in an identified space that is identified on the basis ofexternal information on the self-location or/and region size, such asGPS information, route information, or magnification.

The encoding device or the decoding device gives a lower priority to aspace distant from the self-location than the priority of a nearby spaceto perform encoding or decoding.

The encoding device sets a direction at one of the directions in aworld, in accordance with the magnification or the intended use, toencode a GOS having a layer structure in such direction. Also, thedecoding device decodes a GOS having a layer structure in one of thedirections in a world that has been set in accordance with themagnification or the intended use, preferentially from the bottom layer.

The encoding device changes the accuracy of extracting keypoints, theaccuracy of recognizing objects, or the size of spatial regions, etc.included in a SPC, depending on whether an object is an interior objector an exterior object. Note that the encoding device and the decodingdevice encode or decode an interior GOS and an exterior GOS having closecoordinates in a manner that these GOSs come adjacent to each other in aworld, and associate their identifiers with each other for encoding anddecoding.

Embodiment 2

When using encoded data of a point cloud in an actual device or service,it is desirable that necessary information be transmitted/received inaccordance with the intended use to reduce the network bandwidth.However, there has been no such functionality in the structure ofencoding three-dimensional data, nor an encoding method therefor.

The present embodiment describes a three-dimensional data encodingmethod and a three-dimensional data encoding device for providing thefunctionality of transmitting/receiving only necessary information inencoded data of a three-dimensional point cloud in accordance with theintended use, as well as a three-dimensional data decoding method and athree-dimensional data decoding device for decoding such encoded data.

A voxel (VXL) with a feature greater than or equal to a given amount isdefined as a feature voxel (FVXL), and a world (WLD) constituted byFVXLs is defined as a sparse world (SWLD). FIG. 11 is a diagram showingexample structures of a sparse world and a world. A SWLD includes:FGOSs, each being a GOS constituted by FVXLs; FSPCs, each being a SPCconstituted by FVXLs; and FVLMs, each being a VLM constituted by FVXLs.The data structure and prediction structure of a FGOS, a FSPC, and aFVLM may be the same as those of a GOS, a SPC, and a VLM.

A feature represents the three-dimensional position information on a VXLor the visible-light information on the position of a VXL. A largenumber of features are detected especially at a corner, an edge, etc. ofa three-dimensional object. More specifically, such a feature is athree-dimensional feature or a visible-light feature as described below,but may be any feature that represents the position, luminance, or colorinformation, etc. on a VXL.

Used as three-dimensional features are signature of histograms oforientations (SHOT) features, point feature histograms (PFH) features,or point pair feature (PPF) features.

SHOT features are obtained by dividing the periphery of a VXL, andcalculating an inner product of the reference point and the normalvector of each divided region to represent the calculation result as ahistogram. SHOT features are characterized by a large number ofdimensions and high-level feature representation.

PFH features are obtained by selecting a large number of two point pairsin the vicinity of a VXL, and calculating the normal vector, etc. fromeach two point pair to represent the calculation result as a histogram.PFH features are histogram features, and thus are characterized byrobustness against a certain extent of disturbance and also high-levelfeature representation.

PPF features are obtained by using a normal vector, etc. for each twopoints of VXLs. PPF features, for which all VXLs are used, hasrobustness against occlusion.

Used as visible-light features are scale-invariant feature transform(SIFT), speeded up robust features (SURF), or histogram of orientedgradients (HOG), etc. that use information on an image such as luminancegradient information.

A SWLD is generated by calculating the above-described features of therespective VXLs in a WLD to extract FVXLs. Here, the SWLD may be updatedevery time the WLD is updated, or may be regularly updated after theelapse of a certain period of time, regardless of the timing at whichthe WLD is updated.

A SWLD may be generated for each type of features. For example,different SWLDs may be generated for the respective types of features,such as SWLD1 based on SHOT features and SWLD2 based on SIFT features sothat SWLDs are selectively used in accordance with the intended use.Also, the calculated feature of each FVXL may be held in each FVXL asfeature information.

Next, the usage of a sparse world (SWLD) will be described. A SWLDincludes only feature voxels (FVXLs), and thus its data size is smallerin general than that of a WLD that includes all VXLs.

In an application that utilizes features for a certain purpose, the useof information on a SWLD instead of a WLD reduces the time required toread data from a hard disk, as well as the bandwidth and the timerequired for data transfer over a network. For example, a WLD and a SWLDare held in a server as map information so that map information to besent is selected between the WLD and the SWLD in accordance with arequest from a client. This reduces the network bandwidth and the timerequired for data transfer. More specific examples will be describedbelow.

FIG. 12 and FIG. 13 are diagrams showing usage examples of a SWLD and aWLD. As FIG. 12 shows, when client 1, which is a vehicle-mounted device,requires map information to use it for self-location determination,client 1 sends to a server a request for obtaining map data forself-location estimation (S301). The server sends to client 1 the SWLDin response to the obtainment request (S302). Client 1 uses the receivedSWLD to determine the self-location (S303). In so doing, client 1obtains VXL information on the periphery of client 1 through variousmeans including a distance sensor such as a rangefinder, as well as astereo camera and a combination of a plurality of monocular cameras.Client 1 then estimates the self-location information from the obtainedVXL information and the SWLD. Here, the self-location informationincludes three-dimensional position information, orientation, etc. ofclient 1.

As FIG. 13 shows, when client 2, which is a vehicle-mounted device,requires map information to use it for rendering a map such as athree-dimensional map, client 2 sends to the server a request forobtaining map data for map rendering (S311). The server sends to client2 the WLD in response to the obtainment request (S312). Client 2 usesthe received WLD to render a map (S313). In so doing, client 2 uses, forexample, image client 2 has captured by a visible-light camera, etc. andthe WLD obtained from the server to create a rendering image, andrenders such created image onto a screen of a car navigation system,etc.

As described above, the server sends to a client a SWLD when thefeatures of the respective VXLs are mainly required such as in the caseof self-location estimation, and sends to a client a WLD when detailedVXL information is required such as in the case of map rendering. Thisallows for an efficient sending/receiving of map data.

Note that a client may self-judge which one of a SWLD and a WLD isnecessary, and request the server to send a SWLD or a WLD. Also, theserver may judge which one of a SWLD and a WLD to send in accordancewith the status of the client or a network.

Next, a method will be described of switching the sending/receivingbetween a sparse world (SWLD) and a world (WLD).

Whether to receive a WLD or a SWLD may be switched in accordance withthe network bandwidth. FIG. 14 is a diagram showing an example operationin such case. For example, when a low-speed network is used that limitsthe usable network bandwidth, such as in a Long-Term Evolution (LTE)environment, a client accesses the server over a low-speed network(S321), and obtains the SWLD from the server as map information (S322).Meanwhile, when a high-speed network is used that has an adequatelybroad network bandwidth, such as in a WiFi environment, a clientaccesses the server over a high-speed network (S323), and obtains theWLD from the server (S324). This enables the client to obtainappropriate map information in accordance with the network bandwidthsuch client is using.

More specifically, a client receives the SWLD over an LTE network whenin outdoors, and obtains the WLD over a WiFi network when in indoorssuch as in a facility. This enables the client to obtain more detailedmap information on indoor environment.

As described above, a client may request for a WLD or a SWLD inaccordance with the bandwidth of a network such client is using.Alternatively, the client may send to the server information indicatingthe bandwidth of a network such client is using, and the server may sendto the client data (the WLD or the SWLD) suitable for such client inaccordance with the information. Alternatively, the server may identifythe network bandwidth the client is using, and send to the client data(the WLD or the SWLD) suitable for such client.

Also, whether to receive a WLD or a SWLD may be switched in accordancewith the speed of traveling. FIG. 15 is a diagram showing an exampleoperation in such case. For example, when traveling at a high speed(S331), a client receives the SWLD from the server (S332). Meanwhile,when traveling at a low speed (S333), the client receives the WLD fromthe server (S334). This enables the client to obtain map informationsuitable to the speed, while reducing the network bandwidth. Morespecifically, when traveling on an expressway, the client receives theSWLD with a small data amount, which enables the update of rough mapinformation at an appropriate speed. Meanwhile, when traveling on ageneral road, the client receives the WLD, which enables the obtainmentof more detailed map information.

As described above, the client may request the server for a WLD or aSWLD in accordance with the traveling speed of such client.Alternatively, the client may send to the server information indicatingthe traveling speed of such client, and the server may send to theclient data (the WLD or the SWLD) suitable to such client in accordancewith the information. Alternatively, the server may identify thetraveling speed of the client to send data (the WLD or the SWLD)suitable to such client.

Also, the client may obtain, from the server, a SWLD first, from whichthe client may obtain a WLD of an important region. For example, whenobtaining map information, the client first obtains a SWLD for rough mapinformation, from which the client narrows to a region in which featuressuch as buildings, signals, or persons appear at high frequency so thatthe client can later obtain a WLD of such narrowed region. This enablesthe client to obtain detailed information on a necessary region, whilereducing the amount of data received from the server.

The server may also create from a WLD different SWLDs for the respectiveobjects, and the client may receive SWLDs in accordance with theintended use. This reduces the network bandwidth. For example, theserver recognizes persons or cars in a WLD in advance, and creates aSWLD of persons and a SWLD of cars. The client, when wishing to obtaininformation on persons around the client, receives the SWLD of persons,and when wising to obtain information on cars, receives the SWLD ofcars. Such types of SWLDs may be distinguished by information (flag, ortype, etc.) added to the header, etc.

Next, the structure and the operation flow of the three-dimensional dataencoding device (e.g., a server) according to the present embodimentwill be described. FIG. 16 is a block diagram of three-dimensional dataencoding device 400 according to the present embodiment. FIG. 17 is aflowchart of three-dimensional data encoding processes performed bythree-dimensional data encoding device 400.

Three-dimensional data encoding device 400 shown in FIG. 16 encodesinput three-dimensional data 411, thereby generating encodedthree-dimensional data 413 and encoded three-dimensional data 414, eachbeing an encoded stream. Here, encoded three-dimensional data 413 isencoded three-dimensional data corresponding to a WLD, and encodedthree-dimensional data 414 is encoded three-dimensional datacorresponding to a SWLD. Such three-dimensional data encoding device 400includes, obtainer 401, encoding region determiner 402, SWLD extractor403, WLD encoder 404, and SWLD encoder 405.

First, as FIG. 17 shows, obtainer 401 obtains input three-dimensionaldata 411, which is point group data in a three-dimensional space (S401).

Next, encoding region determiner 402 determines a current spatial regionfor encoding on the basis of a spatial region in which the point clouddata is present (S402).

Next, SWLD extractor 403 defines the current spatial region as a WLD,and calculates the feature from each VXL included in the WLD. Then, SWLDextractor 403 extracts VXLs having an amount of features greater than orequal to a predetermined threshold, defines the extracted VXLs as FVXLs,and adds such FVXLs to a SWLD, thereby generating extractedthree-dimensional data 412 (S403). Stated differently, extractedthree-dimensional data 412 having an amount of features greater than orequal to the threshold is extracted from input three-dimensional data411.

Next, WLD encoder 404 encodes input three-dimensional data 411corresponding to the WLD, thereby generating encoded three-dimensionaldata 413 corresponding to the WLD (S404). In so doing, WLD encoder 404adds to the header of encoded three-dimensional data 413 informationthat distinguishes that such encoded three-dimensional data 413 is astream including a WLD.

SWLD encoder 405 encodes extracted three-dimensional data 412corresponding to the SWLD, thereby generating encoded three-dimensionaldata 414 corresponding to the SWLD (S405). In so doing, SWLD encoder 405adds to the header of encoded three-dimensional data 414 informationthat distinguishes that such encoded three-dimensional data 414 is astream including a SWLD.

Note that the process of generating encoded three-dimensional data 413and the process of generating encoded three-dimensional data 414 may beperformed in the reverse order. Also note that a part or all of theseprocesses may be performed in parallel.

A parameter “world_type” is defined, for example, as information addedto each header of encoded three-dimensional data 413 and encodedthree-dimensional data 414. world_type=0 indicates that a streamincludes a WLD, and world_type=1 indicates that a stream includes aSWLD. An increased number of values may be further assigned to define alarger number of types, e.g., world_type=2. Also, one of encodedthree-dimensional data 413 and encoded three-dimensional data 414 mayinclude a specified flag. For example, encoded three-dimensional data414 may be assigned with a flag indicating that such stream includes aSWLD. In such a case, the decoding device can distinguish whether suchstream is a stream including a WLD or a stream including a SWLD inaccordance with the presence/absence of the flag.

Also, an encoding method used by WLD encoder 404 to encode a WLD may bedifferent from an encoding method used by SWLD encoder 405 to encode aSWLD.

For example, data of a SWLD is decimated, and thus can have a lowercorrelation with the neighboring data than that of a WLD. For thisreason, of intra prediction and inter prediction, inter prediction maybe more preferentially performed in an encoding method used for a SWLDthan in an encoding method used for a WLD.

Also, an encoding method used for a SWLD and an encoding method used fora WLD may represent three-dimensional positions differently. Forexample, three-dimensional coordinates may be used to represent thethree-dimensional positions of FVXLs in a SWLD and an octree describedbelow may be used to represent three-dimensional positions in a WLD, andvice versa.

Also, SWLD encoder 405 performs encoding in a manner that encodedthree-dimensional data 414 of a SWLD has a smaller data size than thedata size of encoded three-dimensional data 413 of a WLD. A SWLD canhave a lower inter-data correlation, for example, than that of a WLD asdescribed above. This can lead to a decreased encoding efficiency, andthus to encoded three-dimensional data 414 having a larger data sizethan the data size of encoded three-dimensional data 413 of a WLD. Whenthe data size of the resulting encoded three-dimensional data 414 islarger than the data size of encoded three-dimensional data 413 of aWLD, SWLD encoder 405 performs encoding again to re-generate encodedthree-dimensional data 414 having a reduced data size.

For example, SWLD extractor 403 re-generates extracted three-dimensionaldata 412 having a reduced number of keypoints to be extracted, and SWLDencoder 405 encodes such extracted three-dimensional data 412.Alternatively, SWLD encoder 405 may perform more coarse quantization.More coarse quantization is achieved, for example, by rounding the datain the lowermost level in an octree structure described below.

When failing to decrease the data size of encoded three-dimensional data414 of the SWLD to smaller than the data size of encodedthree-dimensional data 413 of the WLD, SWLD encoder 405 may not generateencoded three-dimensional data 414 of the SWLD. Alternatively, encodedthree-dimensional data 413 of the WLD may be copied as encodedthree-dimensional data 414 of the SWLD. Stated differently, encodedthree-dimensional data 413 of the WLD may be used as it is as encodedthree-dimensional data 414 of the SWLD.

Next, the structure and the operation flow of the three-dimensional datadecoding device (e.g., a client) according to the present embodimentwill be described. FIG. 18 is a block diagram of three-dimensional datadecoding device 500 according to the present embodiment. FIG. 19 is aflowchart of three-dimensional data decoding processes performed bythree-dimensional data decoding device 500.

Three-dimensional data decoding device 500 shown in FIG. 18 decodesencoded three-dimensional data 511, thereby generating decodedthree-dimensional data 512 or decoded three-dimensional data 513.Encoded three-dimensional data 511 here is, for example, encodedthree-dimensional data 413 or encoded three-dimensional data 414generated by three-dimensional data encoding device 400.

Such three-dimensional data decoding device 500 includes obtainer 501,header analyzer 502, WLD decoder 503, and SWLD decoder 504.

First, as FIG. 19 shows, obtainer 501 obtains encoded three-dimensionaldata 511 (S501). Next, header analyzer 502 analyzes the header ofencoded three-dimensional data 511 to identify whether encodedthree-dimensional data 511 is a stream including a WLD or a streamincluding a SWLD (S502). For example, the above-described parameterworld_type is referred to in making such identification.

When encoded three-dimensional data 511 is a stream including a WLD (Yesin S503), WLD decoder 503 decodes encoded three-dimensional data 511,thereby generating decoded three-dimensional data 512 of the WLD (S504).Meanwhile, when encoded three-dimensional data 511 is a stream includinga SWLD (No in S503), SWLD decoder 504 decodes encoded three-dimensionaldata 511, thereby generating decoded three-dimensional data 513 of theSWLD (S505).

Also, as in the case of the encoding device, a decoding method used byWLD decoder 503 to decode a WLD may be different from a decoding methodused by SWLD decoder 504 to decode a SWLD. For example, of intraprediction and inter prediction, inter prediction may be morepreferentially performed in a decoding method used for a SWLD than in adecoding method used for a WLD.

Also, a decoding method used for a SWLD and a decoding method used for aWLD may represent three-dimensional positions differently. For example,three-dimensional coordinates may be used to represent thethree-dimensional positions of FVXLs in a SWLD and an octree describedbelow may be used to represent three-dimensional positions in a WLD, andvice versa.

Next, an octree representation will be described, which is a method ofrepresenting three-dimensional positions. VXL data included inthree-dimensional data is converted into an octree structure beforeencoded. FIG. 20 is a diagram showing example VXLs in a WLD. FIG. 21 isa diagram showing an octree structure of the WLD shown in FIG. 20 . Anexample shown in FIG. 20 illustrates three VXLs 1 to 3 that includepoint groups (hereinafter referred to as effective VXLs). As FIG. 21shows, the octree structure is made of nodes and leaves. Each node has amaximum of eight nodes or leaves. Each leaf has VXL information. Here,of the leaves shown in FIG. 21 , leaf 1, leaf 2, and leaf 3 representVXL1, VXL2, and VXL3 shown in FIG. 20 , respectively.

More specifically, each node and each leaf correspond to athree-dimensional position. Node 1 corresponds to the entire block shownin FIG. 20 . The block that corresponds to node 1 is divided into eightblocks. Of these eight blocks, blocks including effective VXLs are setas nodes, while the other blocks are set as leaves. Each block thatcorresponds to a node is further divided into eight nodes or leaves.These processes are repeated by the number of times that is equal to thenumber of levels in the octree structure. All blocks in the lowermostlevel are set as leaves.

FIG. 22 is a diagram showing an example SWLD generated from the WLDshown in FIG. 20 . VXL1 and VXL2 shown in FIG. 20 are judged as FVXL1and FVXL2 as a result of feature extraction, and thus are added to theSWLD. Meanwhile, VXL3 is not judged as a FVXL, and thus is not added tothe SWLD. FIG. 23 is a diagram showing an octree structure of the SWLDshown in FIG. 22 . In the octree structure shown in FIG. 23 , leaf 3corresponding to VXL3 shown in FIG. 21 is deleted. Consequently, node 3shown in FIG. 21 has lost an effective VXL, and has changed to a leaf.As described above, a SWLD has a smaller number of leaves in generalthan a WLD does, and thus the encoded three-dimensional data of the SWLDis smaller than the encoded three-dimensional data of the WLD.

The following describes variations of the present embodiment.

For self-location estimation, for example, a client, being avehicle-mounted device, etc., may receive a SWLD from the server to usesuch SWLD to estimate the self-location. Meanwhile, for obstacledetection, the client may detect obstacles by use of three-dimensionalinformation on the periphery obtained by such client through variousmeans including a distance sensor such as a rangefinder, as well as astereo camera and a combination of a plurality of monocular cameras.

In general, a SWLD is less likely to include VXL data on a flat region.As such, the server may hold a subsample world (subWLD) obtained bysubsampling a WLD for detection of static obstacles, and send to theclient the SWLD and the subWLD. This enables the client to performself-location estimation and obstacle detection on the client's part,while reducing the network bandwidth.

When the client renders three-dimensional map data at a high speed, mapinformation having a mesh structure is more useful in some cases. Assuch, the server may generate a mesh from a WLD to hold it beforehand asa mesh world (MWLD). For example, when wishing to perform coarsethree-dimensional rendering, the client receives a MWLD, and whenwishing to perform detailed three-dimensional rendering, the clientreceives a WLD. This reduces the network bandwidth.

In the above description, the server sets, as FVXLs, VXLs having anamount of features greater than or equal to the threshold, but theserver may calculate FVXLs by a different method. For example, theserver may judge that a VXL, a VLM, a SPC, or a GOS that constitutes asignal, or an intersection, etc. as necessary for self-locationestimation, driving assist, or self-driving, etc., and incorporate suchVXL, VLM, SPC, or GOS into a SWLD as a FVXL, a FVLM, a FSPC, or a FGOS.Such judgment may be made manually. Also, FVXLs, etc. that have been seton the basis of an amount of features may be added to FVXLs, etc.obtained by the above method. Stated differently, SWLD extractor 403 mayfurther extract, from input three-dimensional data 411, datacorresponding to an object having a predetermined attribute as extractedthree-dimensional data 412.

Also, that a VXL, a VLM, a SPC, or a GOS is necessary for such intendedusage may be labeled separately from the features. The server mayseparately hold, as an upper layer of a SWLD (e.g., a lane world), FVXLsof a signal or an intersection, etc. necessary for self-locationestimation, driving assist, or self-driving, etc.

The server may also add an attribute to VXLs in a WLD on a random accessbasis or on a predetermined unit basis. An attribute, for example,includes information indicating whether VXLs are necessary forself-location estimation, or information indicating whether VXLs areimportant as traffic information such as a signal, or an intersection,etc. An attribute may also include a correspondence between VXLs andfeatures (intersection, or road, etc.) in lane information (geographicdata files (GDF), etc.).

A method as described below may be used to update a WLD or a SWLD.

Update information indicating changes, etc. in a person, a roadwork, ora tree line (for trucks) is uploaded to the server as point groups ormeta data. The server updates a WLD on the basis of such uploadedinformation, and then updates a SWLD by use of the updated WLD.

The client, when detecting a mismatch between the three-dimensionalinformation such client has generated at the time of self-locationestimation and the three-dimensional information received from theserver, may send to the server the three-dimensional information suchclient has generated, together with an update notification. In such acase, the server updates the SWLD by use of the WLD. When the SWLD isnot to be updated, the server judges that the WLD itself is old.

In the above description, information that distinguishes whether anencoded stream is that of a WLD or a SWLD is added as header informationof the encoded stream. However, when there are many types of worlds suchas a mesh world and a lane world, information that distinguishes thesetypes of the worlds may be added to header information. Also, when thereare many SWLDs with different amounts of features, information thatdistinguishes the respective SWLDs may be added to header information.

In the above description, a SWLD is constituted by FVXLs, but a SWLD mayinclude VXLs that have not been judged as FVXLs. For example, a SWLD mayinclude an adjacent VXL used to calculate the feature of a FVXL. Thisenables the client to calculate the feature of a FVXL when receiving aSWLD, even in the case where feature information is not added to eachFVXL of the SWLD. In such a case, the SWLD may include information thatdistinguishes whether each VXL is a FVXL or a VXL.

As described above, three-dimensional data encoding device 400 extracts,from input three-dimensional data 411 (first three-dimensional data),extracted three-dimensional data 412 (second three-dimensional data)having an amount of a feature greater than or equal to a threshold, andencodes extracted three-dimensional data 412 to generate encodedthree-dimensional data 414 (first encoded three-dimensional data).

This three-dimensional data encoding device 400 generates encodedthree-dimensional data 414 that is obtained by encoding data having anamount of a feature greater than or equal to the threshold. This reducesthe amount of data compared to the case where input three-dimensionaldata 411 is encoded as it is. Three-dimensional data encoding device 400is thus capable of reducing the amount of data to be transmitted.

Three-dimensional data encoding device 400 further encodes inputthree-dimensional data 411 to generate encoded three-dimensional data413 (second encoded three-dimensional data).

This three-dimensional data encoding device 400 enables selectivetransmission of encoded three-dimensional data 413 and encodedthree-dimensional data 414, in accordance, for example, with theintended use, etc.

Also, extracted three-dimensional data 412 is encoded by a firstencoding method, and input three-dimensional data 411 is encoded by asecond encoding method different from the first encoding method.

This three-dimensional data encoding device 400 enables the use of anencoding method suitable for each of input three-dimensional data 411and extracted three-dimensional data 412.

Also, of intra prediction and inter prediction, the inter prediction ismore preferentially performed in the first encoding method than in thesecond encoding method.

This three-dimensional data encoding device 400 enables inter predictionto be more preferentially performed on extracted three-dimensional data412 in which adjacent data items are likely to have low correlation.

Also, the first encoding method and the second encoding method representthree-dimensional positions differently. For example, the secondencoding method represents three-dimensional positions by octree, andthe first encoding method represents three-dimensional positions bythree-dimensional coordinates.

This three-dimensional data encoding device 400 enables the use of amore suitable method to represent the three-dimensional positions ofthree-dimensional data in consideration of the difference in the numberof data items (the number of VXLs or FVXLs) included.

Also, at least one of encoded three-dimensional data 413 and encodedthree-dimensional data 414 includes an identifier indicating whether theencoded three-dimensional data is encoded three-dimensional dataobtained by encoding input three-dimensional data 411 or encodedthree-dimensional data obtained by encoding part of inputthree-dimensional data 411. Stated differently, such identifierindicates whether the encoded three-dimensional data is encodedthree-dimensional data 413 of a WLD or encoded three-dimensional data414 of a SWLD.

This enables the decoding device to readily judge whether the obtainedencoded three-dimensional data is encoded three-dimensional data 413 orencoded three-dimensional data 414.

Also, three-dimensional data encoding device 400 encodes extractedthree-dimensional data 412 in a manner that encoded three-dimensionaldata 414 has a smaller data amount than a data amount of encodedthree-dimensional data 413.

This three-dimensional data encoding device 400 enables encodedthree-dimensional data 414 to have a smaller data amount than the dataamount of encoded three-dimensional data 413.

Also, three-dimensional data encoding device 400 further extracts datacorresponding to an object having a predetermined attribute from inputthree-dimensional data 411 as extracted three-dimensional data 412. Theobject having a predetermined attribute is, for example, an objectnecessary for self-location estimation, driving assist, or self-driving,etc., or more specifically, a signal, an intersection, etc.

This three-dimensional data encoding device 400 is capable of generatingencoded three-dimensional data 414 that includes data required by thedecoding device.

Also, three-dimensional data encoding device 400 (server) further sends,to a client, one of encoded three-dimensional data 413 and encodedthree-dimensional data 414 in accordance with a status of the client.

This three-dimensional data encoding device 400 is capable of sendingappropriate data in accordance with the status of the client.

Also, the status of the client includes one of a communication condition(e.g., network bandwidth) of the client and a traveling speed of theclient.

Also, three-dimensional data encoding device 400 further sends, to aclient, one of encoded three-dimensional data 413 and encodedthree-dimensional data 414 in accordance with a request from the client.

This three-dimensional data encoding device 400 is capable of sendingappropriate data in accordance with the request from the client.

Also, three-dimensional data decoding device 500 according to thepresent embodiment decodes encoded three-dimensional data 413 or encodedthree-dimensional data 414 generated by three-dimensional data encodingdevice 400 described above.

Stated differently, three-dimensional data decoding device 500 decodes,by a first decoding method, encoded three-dimensional data 414 obtainedby encoding extracted three-dimensional data 412 having an amount of afeature greater than or equal to a threshold, extractedthree-dimensional data 412 having been extracted from inputthree-dimensional data 411. Three-dimensional data decoding device 500also decodes, by a second decoding method, encoded three-dimensionaldata 413 obtained by encoding input three-dimensional data 411, thesecond decoding method being different from the first decoding method.

This three-dimensional data decoding device 500 enables selectivereception of encoded three-dimensional data 414 obtained by encodingdata having an amount of a feature greater than or equal to thethreshold and encoded three-dimensional data 413, in accordance, forexample, with the intended use, etc. Three-dimensional data decodingdevice 500 is thus capable of reducing the amount of data to betransmitted. Such three-dimensional data decoding device 500 furtherenables the use of a decoding method suitable for each of inputthree-dimensional data 411 and extracted three-dimensional data 412.

Also, of intra prediction and inter prediction, the inter prediction ismore preferentially performed in the first decoding method than in thesecond decoding method.

This three-dimensional data decoding device 500 enables inter predictionto be more preferentially performed on the extracted three-dimensionaldata in which adjacent data items are likely to have low correlation.

Also, the first decoding method and the second decoding method representthree-dimensional positions differently. For example, the seconddecoding method represents three-dimensional positions by octree, andthe first decoding method represents three-dimensional positions bythree-dimensional coordinates.

This three-dimensional data decoding device 500 enables the use of amore suitable method to represent the three-dimensional positions ofthree-dimensional data in consideration of the difference in the numberof data items (the number of VXLs or FVXLs) included.

Also, at least one of encoded three-dimensional data 413 and encodedthree-dimensional data 414 includes an identifier indicating whether theencoded three-dimensional data is encoded three-dimensional dataobtained by encoding input three-dimensional data 411 or encodedthree-dimensional data obtained by encoding part of inputthree-dimensional data 411. Three-dimensional data decoding device 500refers to such identifier in identifying between encodedthree-dimensional data 413 and encoded three-dimensional data 414.

This three-dimensional data decoding device 500 is capable of readilyjudging whether the obtained encoded three-dimensional data is encodedthree-dimensional data 413 or encoded three-dimensional data 414.

Three-dimensional data decoding device 500 further notifies a server ofa status of the client (three-dimensional data decoding device 500).Three-dimensional data decoding device 500 receives one of encodedthree-dimensional data 413 and encoded three-dimensional data 414 fromthe server, in accordance with the status of the client.

This three-dimensional data decoding device 500 is capable of receivingappropriate data in accordance with the status of the client.

Also, the status of the client includes one of a communication condition(e.g., network bandwidth) of the client and a traveling speed of theclient.

Three-dimensional data decoding device 500 further makes a request ofthe server for one of encoded three-dimensional data 413 and encodedthree-dimensional data 414, and receives one of encodedthree-dimensional data 413 and encoded three-dimensional data 414 fromthe server, in accordance with the request.

This three-dimensional data decoding device 500 is capable of receivingappropriate data in accordance with the intended use.

Embodiment 3

The present embodiment will describe a method of transmitting/receivingthree-dimensional data between vehicles. For example, thethree-dimensional data is transmitted/received between the own vehicleand the nearby vehicle.

FIG. 24 is a block diagram of three-dimensional data creation device 620according to the present embodiment. Such three-dimensional datacreation device 620, which is included, for example, in the own vehicle,mergers first three-dimensional data 632 created by three-dimensionaldata creation device 620 with the received second three-dimensional data635, thereby creating third three-dimensional data 636 having a higherdensity.

Such three-dimensional data creation device 620 includesthree-dimensional data creator 621, request range determiner 622,searcher 623, receiver 624, decoder 625, and merger 626.

First, three-dimensional data creator 621 creates firstthree-dimensional data 632 by use of sensor information 631 detected bythe sensor included in the own vehicle. Next, request range determiner622 determines a request range, which is the range of athree-dimensional space, the data on which is insufficient in thecreated first three-dimensional data 632.

Next, searcher 623 searches for the nearby vehicle having thethree-dimensional data of the request range, and sends request rangeinformation 633 indicating the request range to nearby vehicle 601having been searched out (S623). Next, receiver 624 receives encodedthree-dimensional data 634, which is an encoded stream of the requestrange, from nearby vehicle 601 (S624). Note that searcher 623 mayindiscriminately send requests to all vehicles included in a specifiedrange to receive encoded three-dimensional data 634 from a vehicle thathas responded to the request. Searcher 623 may send a request not onlyto vehicles but also to an object such as a signal and a sign, andreceive encoded three-dimensional data 634 from the object.

Next, decoder 625 decodes the received encoded three-dimensional data634, thereby obtaining second three-dimensional data 635. Next, merger626 merges first three-dimensional data 632 with secondthree-dimensional data 635, thereby creating three-dimensional data 636having a higher density.

Next, the structure and operations of three-dimensional datatransmission device 640 according to the present embodiment will bedescribed. FIG. 25 is a block diagram of three-dimensional datatransmission device 640.

Three-dimensional data transmission device 640 is included, for example,in the above-described nearby vehicle. Three-dimensional datatransmission device 640 processes fifth three-dimensional data 652created by the nearby vehicle into sixth three-dimensional data 654requested by the own vehicle, encodes sixth three-dimensional data 654to generate encoded three-dimensional data 634, and sends encodedthree-dimensional data 634 to the own vehicle.

Three-dimensional data transmission device 640 includesthree-dimensional data creator 641, receiver 642, extractor 643, encoder644, and transmitter 645.

First, three-dimensional data creator 641 creates fifththree-dimensional data 652 by use of sensor information 651 detected bythe sensor included in the nearby vehicle. Next, receiver 642 receivesrequest range information 633 from the own vehicle.

Next, extractor 643 extracts from fifth three-dimensional data 652 thethree-dimensional data of the request range indicated by request rangeinformation 633, thereby processing fifth three-dimensional data 652into sixth three-dimensional data 654. Next, encoder 644 encodes sixththree-dimensional data 654 to generate encoded three-dimensional data643, which is an encoded stream. Then, transmitter 645 sends encodedthree-dimensional data 634 to the own vehicle.

Note that although an example case is described here in which the ownvehicle includes three-dimensional data creation device 620 and thenearby vehicle includes three-dimensional data transmission device 640,each of the vehicles may include the functionality of boththree-dimensional data creation device 620 and three-dimensional datatransmission device 640.

Embodiment 4

The present embodiment describes operations performed in abnormal caseswhen self-location estimation is performed on the basis of athree-dimensional map.

A three-dimensional map is expected to find its expanded use inself-driving of a vehicle and autonomous movement, etc. of a mobileobject such as a robot and a flying object (e.g., a drone). Examplemeans for enabling such autonomous movement include a method in which amobile object travels in accordance with a three-dimensional map, whileestimating its self-location on the map (self-location estimation).

The self-location estimation is enabled by matching a three-dimensionalmap with three-dimensional information on the surrounding of the ownvehicle (hereinafter referred to as self-detected three-dimensionaldata) obtained by a sensor equipped in the own vehicle, such as arangefinder (e.g., a LiDAR) and a stereo camera to estimate the locationof the own vehicle on the three-dimensional map.

As in the case of an HD map suggested by HERE Technologies, for example,a three-dimensional map may include not only a three-dimensional pointcloud, but also two-dimensional map data such as information on theshapes of roads and intersections, or information that changes inreal-time such as information on a traffic jam and an accident. Athree-dimensional map includes a plurality of layers such as layers ofthree-dimensional data, two-dimensional data, and meta-data that changesin real-time, from among which the device can obtain or refer to onlynecessary data.

Point cloud data may be a SWLD as described above, or may include pointgroup data that is different from keypoints. The transmission/receptionof point cloud data is basically carried out in one or more randomaccess units.

A method described below is used as a method of matching athree-dimensional map with self-detected three-dimensional data. Forexample, the device compares the shapes of the point groups in eachother's point clouds, and determines that portions having a high degreeof similarity among keypoints correspond to the same position. When thethree-dimensional map is formed by a SWLD, the device also performsmatching by comparing the keypoints that form the SWLD withthree-dimensional keypoints extracted from the self-detectedthree-dimensional data.

Here, to enable highly accurate self-location estimation, the followingneeds to be satisfied: (A) the three-dimensional map and theself-detected three-dimensional data have been already obtained; and (B)their accuracies satisfy a predetermined requirement. However, one of(A) and (B) cannot be satisfied in abnormal cases such as ones describedbelow.

1. A three-dimensional map is unobtainable over communication.

2. A three-dimensional map is not present, or a three-dimensional maphaving been obtained is corrupt.

3. A sensor of the own vehicle has trouble, or the accuracy of thegenerated self-detected three-dimensional data is inadequate due to badweather.

The following describes operations to cope with such abnormal cases. Thefollowing description illustrates an example case of a vehicle, but themethod described below is applicable to mobile objects on the whole thatare capable of autonomous movement, such as a robot and a drone.

The following describes the structure of the three-dimensionalinformation processing device and its operation according to the presentembodiment capable of coping with abnormal cases regarding athree-dimensional map or self-detected three-dimensional data. FIG. 26is a block diagram of an example structure of three-dimensionalinformation processing device 700 according to the present embodiment.

Three-dimensional information processing device 700 is equipped, forexample, in a mobile object such as a car. As shown in FIG. 26 ,three-dimensional information processing device 700 includesthree-dimensional map obtainer 701, self-detected data obtainer 702,abnormal case judgment unit 703, coping operation determiner 704, andoperation controller 705.

Note that three-dimensional information processing device 700 mayinclude a non-illustrated two-dimensional or one-dimensional sensor thatdetects a structural object or a mobile object around the own vehicle,such as a camera capable of obtaining two-dimensional images and asensor for one-dimensional data utilizing ultrasonic or laser.Three-dimensional information processing device 700 may also include anon-illustrated communication unit that obtains a three-dimensional mapover a mobile communication network, such as 4G and 5G, or viainter-vehicle communication or road-to-vehicle communication.

Three-dimensional map obtainer 701 obtains three-dimensional map 711 ofthe surroundings of the traveling route. For example, three-dimensionalmap obtainer 701 obtains three-dimensional map 711 over a mobilecommunication network, or via inter-vehicle communication orroad-to-vehicle communication.

Next, self-detected data obtainer 702 obtains self-detectedthree-dimensional data 712 on the basis of sensor information. Forexample, self-detected data obtainer 702 generates self-detectedthree-dimensional data 712 on the basis of the sensor informationobtained by a sensor equipped in the own vehicle.

Next, abnormal case judgment unit 703 conducts a predetermined check ofat least one of obtained three-dimensional map 711 and self-detectedthree-dimensional data 712 to detect an abnormal case. Stateddifferently, abnormal case judgment unit 703 judges whether at least oneof obtained three-dimensional map 711 and self-detectedthree-dimensional data 712 is abnormal.

When the abnormal case is detected, coping operation determiner 704determines a coping operation to cope with such abnormal case. Next,operation controller 705 controls the operation of each of theprocessing units necessary to perform the coping operation.

Meanwhile, when no abnormal case is detected, three-dimensionalinformation processing device 700 terminates the process.

Also, three-dimensional information processing device 700 estimates thelocation of the vehicle equipped with three-dimensional informationprocessing device 700, using three-dimensional map 711 and self-detectedthree-dimensional data 712. Next, three-dimensional informationprocessing device 700 performs the automatic operation of the vehicle byuse of the estimated location of the vehicle.

As described above, three-dimensional information processing device 700obtains, via a communication channel, map data (three-dimensional map711) that includes first three-dimensional position information. Thefirst three-dimensional position information includes, for example, aplurality of random access units, each of which is an assembly of atleast one subspace and is individually decodable, the at least onesubspace having three-dimensional coordinates information and serving asa unit in which each of the plurality of random access units is encoded.The first three-dimensional position information is, for example, data(SWLD) obtained by encoding keypoints, each of which has an amount of athree-dimensional feature greater than or equal to a predeterminedthreshold.

Three-dimensional information processing device 700 also generatessecond three-dimensional position information (self-detectedthree-dimensional data 712) from information detected by a sensor.Three-dimensional information processing device 700 then judges whetherone of the first three-dimensional position information and the secondthree-dimensional position information is abnormal by performing, on oneof the first three-dimensional position information and the secondthree-dimensional position information, a process of judging whether anabnormality is present.

Three-dimensional information processing device 700 determines a copingoperation to cope with the abnormality when one of the firstthree-dimensional position information and the second three-dimensionalposition information is judged to be abnormal. Three-dimensionalinformation processing device 700 then executes a control that isrequired to perform the coping operation.

This structure enables three-dimensional information processing device700 to detect an abnormality regarding one of the firstthree-dimensional position information and the second three-dimensionalposition information, and to perform a coping operation therefor.

Embodiment 5

The present embodiment describes a method, etc. of transmittingthree-dimensional data to a following vehicle.

FIG. 27 is a block diagram of an exemplary structure ofthree-dimensional data creation device 810 according to the presentembodiment. Such three-dimensional data creation device 810 is equipped,for example, in a vehicle. Three-dimensional data creation device 810transmits and receives three-dimensional data to and from an externalcloud-based traffic monitoring system, a preceding vehicle, or afollowing vehicle, and creates and stores three-dimensional data.

Three-dimensional data creation device 810 includes data receiver 811,communication unit 812, reception controller 813, format converter 814,a plurality of sensors 815, three-dimensional data creator 816,three-dimensional data synthesizer 817, three-dimensional data storage818, communication unit 819, transmission controller 820, formatconverter 821, and data transmitter 822.

Data receiver 811 receives three-dimensional data 831 from a cloud-basedtraffic monitoring system or a preceding vehicle. Three-dimensional data831 includes, for example, information on a region undetectable bysensors 815 of the own vehicle, such as a point cloud, visible lightvideo, depth information, sensor position information, and speedinformation.

Communication unit 812 communicates with the cloud-based trafficmonitoring system or the preceding vehicle to transmit a datatransmission request, etc. to the cloud-based traffic monitoring systemor the preceding vehicle.

Reception controller 813 exchanges information, such as information onsupported formats, with a communications partner via communication unit812 to establish communication with the communications partner.

Format converter 814 applies format conversion, etc. onthree-dimensional data 831 received by data receiver 811 to generatethree-dimensional data 832. Format converter 814 also decompresses ordecodes three-dimensional data 831 when three-dimensional data 831 iscompressed or encoded.

A plurality of sensors 815 are a group of sensors, such as visible lightcameras and infrared cameras, that obtain information on the outside ofthe vehicle and generate sensor information 833. Sensor information 833is, for example, three-dimensional data such as a point cloud (pointgroup data), when sensors 815 are laser sensors such as LiDARs. Notethat a single sensor may serve as a plurality of sensors 815.

Three-dimensional data creator 816 generates three-dimensional data 834from sensor information 833. Three-dimensional data 834 includes, forexample, information such as a point cloud, visible light video, depthinformation, sensor position information, and speed information.

Three-dimensional data synthesizer 817 synthesizes three-dimensionaldata 834 created on the basis of sensor information 833 of the ownvehicle with three-dimensional data 832 created by the cloud-basedtraffic monitoring system or the preceding vehicle, etc., therebyforming three-dimensional data 835 of a space that includes the spaceahead of the preceding vehicle undetectable by sensors 815 of the ownvehicle.

Three-dimensional data storage 818 stores generated three-dimensionaldata 835, etc.

Communication unit 819 communicates with the cloud-based trafficmonitoring system or the following vehicle to transmit a datatransmission request, etc. to the cloud-based traffic monitoring systemor the following vehicle.

Transmission controller 820 exchanges information such as information onsupported formats with a communications partner via communication unit819 to establish communication with the communications partner.Transmission controller 820 also determines a transmission region, whichis a space of the three-dimensional data to be transmitted, on the basisof three-dimensional data formation information on three-dimensionaldata 832 generated by three-dimensional data synthesizer 817 and thedata transmission request from the communications partner.

More specifically, transmission controller 820 determines a transmissionregion that includes the space ahead of the own vehicle undetectable bya sensor of the following vehicle, in response to the data transmissionrequest from the cloud-based traffic monitoring system or the followingvehicle. Transmission controller 820 judges, for example, whether aspace is transmittable or whether the already transmitted space includesan update, on the basis of the three-dimensional data formationinformation to determine a transmission region. For example,transmission controller 820 determines, as a transmission region, aregion that is: a region specified by the data transmission request; anda region, corresponding three-dimensional data 835 of which is present.Transmission controller 820 then notifies format converter 821 of theformat supported by the communications partner and the transmissionregion.

Of three-dimensional data 835 stored in three-dimensional data storage818, format converter 821 converts three-dimensional data 836 of thetransmission region into the format supported by the receiver end togenerate three-dimensional data 837. Note that format converter 821 maycompress or encode three-dimensional data 837 to reduce the data amount.

Data transmitter 822 transmits three-dimensional data 837 to thecloud-based traffic monitoring system or the following vehicle. Suchthree-dimensional data 837 includes, for example, information on a blindspot, which is a region hidden from view of the following vehicle, suchas a point cloud ahead of the own vehicle, visible light video, depthinformation, and sensor position information.

Note that an example has been described in which format converter 814and format converter 821 perform format conversion, etc., but formatconversion may not be performed.

With the above structure, three-dimensional data creation device 810obtains, from an external device, three-dimensional data 831 of a regionundetectable by sensors 815 of the own vehicle, and synthesizesthree-dimensional data 831 with three-dimensional data 834 that is basedon sensor information 833 detected by sensors 815 of the own vehicle,thereby generating three-dimensional data 835. Three-dimensional datacreation device 810 is thus capable of generating three-dimensional dataof a range undetectable by sensors 815 of the own vehicle.

Three-dimensional data creation device 810 is also capable oftransmitting, to the cloud-based traffic monitoring system or thefollowing vehicle, etc., three-dimensional data of a space that includesthe space ahead of the own vehicle undetectable by a sensor of thefollowing vehicle, in response to the data transmission request from thecloud-based traffic monitoring system or the following vehicle.

Embodiment 6

In embodiment 5, an example is described in which a client device of avehicle or the like transmits three-dimensional data to another vehicleor a server such as a cloud-based traffic monitoring system. In thepresent embodiment, a client device transmits sensor informationobtained through a sensor to a server or a client device.

A structure of a system according to the present embodiment will firstbe described. FIG. 28 is a diagram showing the structure of atransmission/reception system of a three-dimensional map and sensorinformation according to the present embodiment. This system includesserver 901, and client devices 902A and 902B. Note that client devices902A and 902B are also referred to as client device 902 when noparticular distinction is made therebetween.

Client device 902 is, for example, a vehicle-mounted device equipped ina mobile object such as a vehicle. Server 901 is, for example, acloud-based traffic monitoring system, and is capable of communicatingwith the plurality of client devices 902.

Server 901 transmits the three-dimensional map formed by a point cloudto client device 902. Note that a structure of the three-dimensional mapis not limited to a point cloud, and may also be another structureexpressing three-dimensional data such as a mesh structure.

Client device 902 transmits the sensor information obtained by clientdevice 902 to server 901. The sensor information includes, for example,at least one of information obtained by LiDAR, a visible light image, aninfrared image, a depth image, sensor position information, or sensorspeed information.

The data to be transmitted and received between server 901 and clientdevice 902 may be compressed in order to reduce data volume, and mayalso be transmitted uncompressed in order to maintain data precision.When compressing the data, it is possible to use a three-dimensionalcompression method on the point cloud based on, for example, an octreestructure. It is possible to use a two-dimensional image compressionmethod on the visible light image, the infrared image, and the depthimage. The two-dimensional image compression method is, for example,MPEG-4 AVC or HEVC standardized by MPEG.

Server 901 transmits the three-dimensional map managed by server 901 toclient device 902 in response to a transmission request for thethree-dimensional map from client device 902. Note that server 901 mayalso transmit the three-dimensional map without waiting for thetransmission request for the three-dimensional map from client device902. For example, server 901 may broadcast the three-dimensional map toat least one client device 902 located in a predetermined space. Server901 may also transmit the three-dimensional map suited to a position ofclient device 902 at fixed time intervals to client device 902 that hasreceived the transmission request once. Server 901 may also transmit thethree-dimensional map managed by server 901 to client device 902 everytime the three-dimensional map is updated.

Client device 902 sends the transmission request for thethree-dimensional map to server 901. For example, when client device 902wants to perform the self-location estimation during traveling, clientdevice 902 transmits the transmission request for the three-dimensionalmap to server 901.

Note that in the following cases, client device 902 may send thetransmission request for the three-dimensional map to server 901. Clientdevice 902 may send the transmission request for the three-dimensionalmap to server 901 when the three-dimensional map stored by client device902 is old. For example, client device 902 may send the transmissionrequest for the three-dimensional map to server 901 when a fixed periodhas passed since the three-dimensional map is obtained by client device902.

Client device 902 may also send the transmission request for thethree-dimensional map to server 901 before a fixed time when clientdevice 902 exits a space shown in the three-dimensional map stored byclient device 902. For example, client device 902 may send thetransmission request for the three-dimensional map to server 901 whenclient device 902 is located within a predetermined distance from aboundary of the space shown in the three-dimensional map stored byclient device 902. When a movement path and a movement speed of clientdevice 902 are understood, a time when client device 902 exits the spaceshown in the three-dimensional map stored by client device 902 may bepredicted based on the movement path and the movement speed of clientdevice 902.

Client device 902 may also send the transmission request for thethree-dimensional map to server 901 when an error during alignment ofthe three-dimensional data and the three-dimensional map created fromthe sensor information by client device 902 is at least at a fixedlevel.

Client device 902 transmits the sensor information to server 901 inresponse to a transmission request for the sensor information fromserver 901. Note that client device 902 may transmit the sensorinformation to server 901 without waiting for the transmission requestfor the sensor information from server 901. For example, client device902 may periodically transmit the sensor information during a fixedperiod when client device 902 has received the transmission request forthe sensor information from server 901 once. Client device 902 maydetermine that there is a possibility of a change in thethree-dimensional map of a surrounding area of client device 902 havingoccurred, and transmit this information and the sensor information toserver 901, when the error during alignment of the three-dimensionaldata created by client device 902 based on the sensor information andthe three-dimensional map obtained from server 901 is at least at thefixed level.

Server 901 sends a transmission request for the sensor information toclient device 902. For example, server 901 receives positioninformation, such as GPS information, about client device 902 fromclient device 902. Server 901 sends the transmission request for thesensor information to client device 902 in order to generate a newthree-dimensional map, when it is determined that client device 902 isapproaching a space in which the three-dimensional map managed by server901 contains little information, based on the position information aboutclient device 902. Server 901 may also send the transmission request forthe sensor information, when wanting to (i) update the three-dimensionalmap, (ii) check road conditions during snowfall, a disaster, or thelike, or (iii) check traffic congestion conditions, accident/incidentconditions, or the like.

Client device 902 may set an amount of data of the sensor information tobe transmitted to server 901 in accordance with communication conditionsor bandwidth during reception of the transmission request for the sensorinformation to be received from server 901. Setting the amount of dataof the sensor information to be transmitted to server 901 is, forexample, increasing/reducing the data itself or appropriately selectinga compression method.

FIG. 29 is a block diagram showing an example structure of client device902. Client device 902 receives the three-dimensional map formed by apoint cloud and the like from server 901, and estimates a self-locationof client device 902 using the three-dimensional map created based onthe sensor information of client device 902. Client device 902 transmitsthe obtained sensor information to server 901.

Client device 902 includes data receiver 1011, communication unit 1012,reception controller 1013, format converter 1014, sensors 1015,three-dimensional data creator 1016, three-dimensional image processor1017, three-dimensional data storage 1018, format converter 1019,communication unit 1020, transmission controller 1021, and datatransmitter 1022.

Data receiver 1011 receives three-dimensional map 1031 from server 901.Three-dimensional map 1031 is data that includes a point cloud such as aWLD or a SWLD. Three-dimensional map 1031 may include compressed data oruncompressed data.

Communication unit 1012 communicates with server 901 and transmits adata transmission request (e.g. transmission request forthree-dimensional map) to server 901.

Reception controller 1013 exchanges information, such as information onsupported formats, with a communications partner via communication unit1012 to establish communication with the communications partner.

Format converter 1014 performs a format conversion and the like onthree-dimensional map 1031 received by data receiver 1011 to generatethree-dimensional map 1032. Format converter 1014 also performs adecompression or decoding process when three-dimensional map 1031 iscompressed or encoded. Note that format converter 1014 does not performthe decompression or decoding process when three-dimensional map 1031 isuncompressed data.

Sensors 815 are a group of sensors, such as LiDARs, visible lightcameras, infrared cameras, or depth sensors that obtain informationabout the outside of a vehicle equipped with client device 902, andgenerate sensor information 1033. Sensor information 1033 is, forexample, three-dimensional data such as a point cloud (point group data)when sensors 1015 are laser sensors such as LiDARs. Note that a singlesensor may serve as sensors 1015.

Three-dimensional data creator 1016 generates three-dimensional data1034 of a surrounding area of the own vehicle based on sensorinformation 1033. For example, three-dimensional data creator 1016generates point cloud data with color information on the surroundingarea of the own vehicle using information obtained by LiDAR and visiblelight video obtained by a visible light camera.

Three-dimensional image processor 1017 performs a self-locationestimation process and the like of the own vehicle, using (i) thereceived three-dimensional map 1032 such as a point cloud, and (ii)three-dimensional data 1034 of the surrounding area of the own vehiclegenerated using sensor information 1033. Note that three-dimensionalimage processor 1017 may generate three-dimensional data 1035 about thesurroundings of the own vehicle by merging three-dimensional map 1032and three-dimensional data 1034, and may perform the self-locationestimation process using the created three-dimensional data 1035.

Three-dimensional data storage 1018 stores three-dimensional map 1032,three-dimensional data 1034, three-dimensional data 1035, and the like.

Format converter 1019 generates sensor information 1037 by convertingsensor information 1033 to a format supported by a receiver end. Notethat format converter 1019 may reduce the amount of data by compressingor encoding sensor information 1037. Format converter 1019 may omit thisprocess when format conversion is not necessary. Format converter 1019may also control the amount of data to be transmitted in accordance witha specified transmission range.

Communication unit 1020 communicates with server 901 and receives a datatransmission request (transmission request for sensor information) andthe like from server 901.

Transmission controller 1021 exchanges information, such as informationon supported formats, with a communications partner via communicationunit 1020 to establish communication with the communications partner.

Data transmitter 1022 transmits sensor information 1037 to server 901.Sensor information 1037 includes, for example, information obtainedthrough sensors 1015, such as information obtained by LiDAR, a luminanceimage obtained by a visible light camera, an infrared image obtained byan infrared camera, a depth image obtained by a depth sensor, sensorposition information, and sensor speed information.

A structure of server 901 will be described next. FIG. 30 is a blockdiagram showing an example structure of server 901. Server 901 transmitssensor information from client device 902 and creates three-dimensionaldata based on the received sensor information. Server 901 updates thethree-dimensional map managed by server 901 using the createdthree-dimensional data. Server 901 transmits the updatedthree-dimensional map to client device 902 in response to a transmissionrequest for the three-dimensional map from client device 902.

Server 901 includes data receiver 1111, communication unit 1112,reception controller 1113, format converter 1114, three-dimensional datacreator 1116, three-dimensional data merger 1117, three-dimensional datastorage 1118, format converter 1119, communication unit 1120,transmission controller 1121, and data transmitter 1122.

Data receiver 1111 receives sensor information 1037 from client device902. Sensor information 1037 includes, for example, information obtainedby LiDAR, a luminance image obtained by a visible light camera, aninfrared image obtained by an infrared camera, a depth image obtained bya depth sensor, sensor position information, sensor speed information,and the like.

Communication unit 1112 communicates with client device 902 andtransmits a data transmission request (e.g. transmission request forsensor information) and the like to client device 902.

Reception controller 1113 exchanges information, such as information onsupported formats, with a communications partner via communication unit1112 to establish communication with the communications partner.

Format converter 1114 generates sensor information 1132 by performing adecompression or decoding process when received sensor information 1037is compressed or encoded. Note that format converter 1114 does notperform the decompression or decoding process when sensor information1037 is uncompressed data.

Three-dimensional data creator 1116 generates three-dimensional data1134 of a surrounding area of client device 902 based on sensorinformation 1132. For example, three-dimensional data creator 1116generates point cloud data with color information on the surroundingarea of client device 902 using information obtained by LiDAR andvisible light video obtained by a visible light camera.

Three-dimensional data merger 1117 updates three-dimensional map 1135 bymerging three-dimensional data 1134 created based on sensor information1132 with three-dimensional map 1135 managed by server 901.

Three-dimensional data storage 1118 stores three-dimensional map 1135and the like.

Format converter 1119 generates three-dimensional map 1031 by convertingthree-dimensional map 1135 to a format supported by the receiver end.Note that format converter 1119 may reduce the amount of data bycompressing or encoding three-dimensional map 1135. Format converter1119 may omit this process when format conversion is not necessary.Format converter 1119 may also control the amount of data to betransmitted in accordance with a specified transmission range.

Communication unit 1120 communicates with client device 902 and receivesa data transmission request (transmission request for three-dimensionalmap) and the like from client device 902.

Transmission controller 1121 exchanges information, such as informationon supported formats, with a communications partner via communicationunit 1120 to establish communication with the communications partner.

Data transmitter 1122 transmits three-dimensional map 1031 to clientdevice 902. Three-dimensional map 1031 is data that includes a pointcloud such as a WLD or a SWLD. Three-dimensional map 1031 may includeone of compressed data and uncompressed data.

An operational flow of client device 902 will be described next. FIG. 31is a flowchart of an operation when client device 902 obtains thethree-dimensional map.

Client device 902 first requests server 901 to transmit thethree-dimensional map (point cloud, etc.) (S1001). At this point, byalso transmitting the position information about client device 902obtained through GPS and the like, client device 902 may also requestserver 901 to transmit a three-dimensional map relating to this positioninformation.

Client device 902 next receives the three-dimensional map from server901 (S1002). When the received three-dimensional map is compressed data,client device 902 decodes the received three-dimensional map andgenerates an uncompressed three-dimensional map (S1003).

Client device 902 next creates three-dimensional data 1034 of thesurrounding area of client device 902 using sensor information 1033obtained by sensors 1015 (S1004). Client device 902 next estimates theself-location of client device 902 using three-dimensional map 1032received from server 901 and three-dimensional data 1034 created usingsensor information 1033 (S1005).

FIG. 32 is a flowchart of an operation when client device 902 transmitsthe sensor information. Client device 902 first receives a transmissionrequest for the sensor information from server 901 (S1011). Clientdevice 902 that has received the transmission request transmits sensorinformation 1037 to server 901 (S1012). Note that client device 902 maygenerate sensor information 1037 by compressing each piece ofinformation using a compression method suited to each piece ofinformation, when sensor information 1033 includes a plurality of piecesof information obtained by sensors 1015.

An operational flow of server 901 will be described next. FIG. 33 is aflowchart of an operation when server 901 obtains the sensorinformation. Server 901 first requests client device 902 to transmit thesensor information (S1021). Server 901 next receives sensor information1037 transmitted from client device 902 in accordance with the request(S1022). Server 901 next creates three-dimensional data 1134 using thereceived sensor information 1037 (S1023). Server 901 next reflects thecreated three-dimensional data 1134 in three-dimensional map 1135(S1024).

FIG. 34 is a flowchart of an operation when server 901 transmits thethree-dimensional map. Server 901 first receives a transmission requestfor the three-dimensional map from client device 902 (S1031). Server 901that has received the transmission request for the three-dimensional maptransmits the three-dimensional map to client device 902 (S1032). Atthis point, server 901 may extract a three-dimensional map of a vicinityof client device 902 along with the position information about clientdevice 902, and transmit the extracted three-dimensional map. Server 901may compress the three-dimensional map formed by a point cloud using,for example, an octree structure compression method, and transmit thecompressed three-dimensional map.

Hereinafter, variations of the present embodiment will be described.

Server 901 creates three-dimensional data 1134 of a vicinity of aposition of client device 902 using sensor information 1037 receivedfrom client device 902. Server 901 next calculates a difference betweenthree-dimensional data 1134 and three-dimensional map 1135, by matchingthe created three-dimensional data 1134 with three-dimensional map 1135of the same area managed by server 901. Server 901 determines that atype of anomaly has occurred in the surrounding area of client device902, when the difference is greater than or equal to a predeterminedthreshold. For example, it is conceivable that a large difference occursbetween three-dimensional map 1135 managed by server 901 andthree-dimensional data 1134 created based on sensor information 1037,when land subsidence and the like occurs due to a natural disaster suchas an earthquake.

Sensor information 1037 may include information indicating at least oneof a sensor type, a sensor performance, and a sensor model number.Sensor information 1037 may also be appended with a class ID and thelike in accordance with the sensor performance. For example, when sensorinformation 1037 is obtained by LiDAR, it is conceivable to assignidentifiers to the sensor performance. A sensor capable of obtaininginformation with precision in units of several millimeters is class 1, asensor capable of obtaining information with precision in units ofseveral centimeters is class 2, and a sensor capable of obtaininginformation with precision in units of several meters is class 3. Server901 may estimate sensor performance information and the like from amodel number of client device 902. For example, when client device 902is equipped in a vehicle, server 901 may determine sensor specificationinformation from a type of the vehicle. In this case, server 901 mayobtain information on the type of the vehicle in advance, and theinformation may also be included in the sensor information. Server 901may change a degree of correction with respect to three-dimensional data1134 created using sensor information 1037, using obtained sensorinformation 1037. For example, when the sensor performance is high inprecision (class 1), server 901 does not correct three-dimensional data1134. When the sensor performance is low in precision (class 3), server901 corrects three-dimensional data 1134 in accordance with theprecision of the sensor. For example, server 901 increases the degree(intensity) of correction with a decrease in the precision of thesensor.

Server 901 may simultaneously send the transmission request for thesensor information to the plurality of client devices 902 in a certainspace. Server 901 does not need to use all of the sensor information forcreating three-dimensional data 1134 and may, for example, select sensorinformation to be used in accordance with the sensor performance, whenhaving received a plurality of pieces of sensor information from theplurality of client devices 902. For example, when updatingthree-dimensional map 1135, server 901 may select high-precision sensorinformation (class 1) from among the received plurality of pieces ofsensor information, and create three-dimensional data 1134 using theselected sensor information.

Server 901 is not limited to only being a server such as a cloud-basedtraffic monitoring system, and may also be another (vehicle-mounted)client device. FIG. 35 is a diagram of a system structure in this case.

For example, client device 902C sends a transmission request for sensorinformation to client device 902A located nearby, and obtains the sensorinformation from client device 902A. Client device 902C then createsthree-dimensional data using the obtained sensor information of clientdevice 902A, and updates a three-dimensional map of client device 902C.This enables client device 902C to generate a three-dimensional map of aspace that can be obtained from client device 902A, and fully utilizethe performance of client device 902C. For example, such a case isconceivable when client device 902C has high performance.

In this case, client device 902A that has provided the sensorinformation is given rights to obtain the high-precisionthree-dimensional map generated by client device 902C. Client device902A receives the high-precision three-dimensional map from clientdevice 902C in accordance with these rights.

Server 901 may send the transmission request for the sensor informationto the plurality of client devices 902 (client device 902A and clientdevice 902B) located nearby client device 902C. When a sensor of clientdevice 902A or client device 902B has high performance, client device902C is capable of creating the three-dimensional data using the sensorinformation obtained by this high-performance sensor.

FIG. 36 is a block diagram showing a functionality structure of server901 and client device 902. Server 901 includes, for example,three-dimensional map compression/decoding processor 1201 thatcompresses and decodes the three-dimensional map and sensor informationcompression/decoding processor 1202 that compresses and decodes thesensor information.

Client device 902 includes three-dimensional map decoding processor 1211and sensor information compression processor 1212. Three-dimensional mapdecoding processor 1211 receives encoded data of the compressedthree-dimensional map, decodes the encoded data, and obtains thethree-dimensional map. Sensor information compression processor 1212compresses the sensor information itself instead of thethree-dimensional data created using the obtained sensor information,and transmits the encoded data of the compressed sensor information toserver 901. With this structure, client device 902 does not need tointernally store a processor that performs a process for compressing thethree-dimensional data of the three-dimensional map (point cloud, etc.),as long as client device 902 internally stores a processor that performsa process for decoding the three-dimensional map (point cloud, etc.).This makes it possible to limit costs, power consumption, and the likeof client device 902.

As stated above, client device 902 according to the present embodimentis equipped in the mobile object, and creates three-dimensional data1034 of a surrounding area of the mobile object using sensor information1033 that is obtained through sensor 1015 equipped in the mobile objectand indicates a surrounding condition of the mobile object. Clientdevice 902 estimates a self-location of the mobile object using thecreated three-dimensional data 1034. Client device 902 transmitsobtained sensor information 1033 to server 901 or another mobile object.

This enables client device 902 to transmit sensor information 1033 toserver 901 or the like. This makes it possible to further reduce theamount of transmission data compared to when transmitting thethree-dimensional data. Since there is no need for client device 902 toperform processes such as compressing or encoding the three-dimensionaldata, it is possible to reduce the processing amount of client device902. As such, client device 902 is capable of reducing the amount ofdata to be transmitted or simplifying the structure of the device.

Client device 902 further transmits the transmission request for thethree-dimensional map to server 901 and receives three-dimensional map1031 from server 901. In the estimating of the self-location, clientdevice 902 estimates the self-location using three-dimensional data 1034and three-dimensional map 1032.

Sensor information 1034 includes at least one of information obtained bya laser sensor, a luminance image, an infrared image, a depth image,sensor position information, or sensor speed information.

Sensor information 1033 includes information that indicates aperformance of the sensor.

Client device 902 encodes or compresses sensor information 1033, and inthe transmitting of the sensor information, transmits sensor information1037 that has been encoded or compressed to server 901 or another mobileobject 902. This enables client device 902 to reduce the amount of datato be transmitted.

For example, client device 902 includes a processor and memory. Theprocessor performs the above processes using the memory.

Server 901 according to the present embodiment is capable ofcommunicating with client device 902 equipped in the mobile object, andreceives sensor information 1037 that is obtained through sensor 1015equipped in the mobile object and indicates a surrounding condition ofthe mobile object. Server 901 creates three-dimensional data 1134 of asurrounding area of the mobile object using received sensor information1037.

With this, server 901 creates three-dimensional data 1134 using sensorinformation 1037 transmitted from client device 902. This makes itpossible to further reduce the amount of transmission data compared towhen client device 902 transmits the three-dimensional data. Since thereis no need for client device 902 to perform processes such ascompressing or encoding the three-dimensional data, it is possible toreduce the processing amount of client device 902. As such, server 901is capable of reducing the amount of data to be transmitted orsimplifying the structure of the device.

Server 901 further transmits a transmission request for the sensorinformation to client device 902.

Server 901 further updates three-dimensional map 1135 using the createdthree-dimensional data 1134, and transmits three-dimensional map 1135 toclient device 902 in response to the transmission request forthree-dimensional map 1135 from client device 902.

Sensor information 1037 includes at least one of information obtained bya laser sensor, a luminance image, an infrared image, a depth image,sensor position information, or sensor speed information.

Sensor information 1037 includes information that indicates aperformance of the sensor.

Server 901 further corrects the three-dimensional data in accordancewith the performance of the sensor. This enables the three-dimensionaldata creation method to improve the quality of the three-dimensionaldata.

In the receiving of the sensor information, server 901 receives aplurality of pieces of sensor information 1037 received from a pluralityof client devices 902, and selects sensor information 1037 to be used inthe creating of three-dimensional data 1134, based on a plurality ofpieces of information that each indicates the performance of the sensorincluded in the plurality of pieces of sensor information 1037. Thisenables server 901 to improve the quality of three-dimensional data1134.

Server 901 decodes or decompresses received sensor information 1037, andcreates three-dimensional data 1134 using sensor information 1132 thathas been decoded or decompressed. This enables server 901 to reduce theamount of data to be transmitted.

For example, server 901 includes a processor and memory. The processorperforms the above processes using the memory.

Embodiment 7

In the present embodiment, three-dimensional data encoding and decodingmethods using an inter prediction process will be described.

FIG. 37 is a block diagram of three-dimensional data encoding device1300 according to the present embodiment. This three-dimensional dataencoding device 1300 generates an encoded bitstream (hereinafter, alsosimply referred to as bitstream) that is an encoded signal, by encodingthree-dimensional data. As illustrated in FIG. 37 , three-dimensionaldata encoding device 1300 includes divider 1301, subtractor 1302,transformer 1303, quantizer 1304, inverse quantizer 1305, inversetransformer 1306, adder 1307, reference volume memory 1308, intrapredictor 1309, reference space memory 1310, inter predictor 1311,prediction controller 1312, and entropy encoder 1313.

Divider 1301 divides a plurality of volumes (VLMs) that are encodingunits of each space (SPC) included in the three-dimensional data.Divider 1301 makes an octree representation (make into an octree) ofvoxels in each volume. Note that divider 1301 may make the spaces intoan octree representation with the spaces having the same size as thevolumes. Divider 1301 may also append information (depth information,etc.) necessary for making the octree representation to a header and thelike of a bitstream.

Subtractor 1302 calculates a difference between a volume (encodingtarget volume) outputted by divider 1301 and a predicted volumegenerated through intra prediction or inter prediction, which will bedescribed later, and outputs the calculated difference to transformer1303 as a prediction residual. FIG. 38 is a diagram showing an examplecalculation of the prediction residual. Note that bit sequences of theencoding target volume and the predicted volume shown here are, forexample, position information indicating positions of three-dimensionalpoints included in the volumes.

Hereinafter, a scan order of an octree representation and voxels will bedescribed. A volume is encoded after being converted into an octreestructure (made into an octree). The octree structure includes nodes andleaves. Each node has eight nodes or leaves, and each leaf has voxel(VXL) information. FIG. 39 is a diagram showing an example structure ofa volume including voxels. FIG. 40 is a diagram showing an example ofthe volume shown in FIG. 39 having been converted into the octreestructure. Among the leaves shown in FIG. 40 , leaves 1, 2, and 3respectively represent VXL 1, VXL 2, and VXL 3, and represent VXLsincluding a point group (hereinafter, active VXLs).

An octree is represented by, for example, binary sequences of 1s and 0s.For example, when giving the nodes or the active VXLs a value of 1 andeverything else a value of 0, each node and leaf is assigned with thebinary sequence shown in FIG. 40 . Thus, this binary sequence is scannedin accordance with a breadth-first or a depth-first scan order. Forexample, when scanning breadth-first, the binary sequence shown in A ofFIG. 41 is obtained. When scanning depth-first, the binary sequenceshown in B of FIG. 41 is obtained. The binary sequences obtained throughthis scanning are encoded through entropy encoding, which reduces anamount of information.

Depth information in the octree representation will be described next.Depth in the octree representation is used in order to control up to howfine a granularity point cloud information included in a volume isstored. Upon setting a great depth, it is possible to reproduce thepoint cloud information to a more precise level, but an amount of datafor representing the nodes and leaves increases. Upon setting a smalldepth, however, the amount of data decreases, but some information thatthe point cloud information originally held is lost, since pieces ofpoint cloud information including different positions and differentcolors are now considered as pieces of point cloud information includingthe same position and the same color.

For example, FIG. 42 is a diagram showing an example in which the octreewith a depth of 2 shown in FIG. 40 is represented with a depth of 1. Theoctree shown in FIG. 42 has a lower amount of data than the octree shownin FIG. 40 . In other words, the binarized octree shown in FIG. 42 has alower bit count than the octree shown in FIG. 40 . Leaf 1 and leaf 2shown in FIG. 40 are represented by leaf 1 shown in FIG. 41 . In otherwords, the information on leaf 1 and leaf 2 being in different positionsis lost.

FIG. 43 is a diagram showing a volume corresponding to the octree shownin FIG. 42 . VXL 1 and VXL 2 shown in FIG. 39 correspond to VXL 12 shownin FIG. 43 . In this case, three-dimensional data encoding device 1300generates color information of VXL 12 shown in FIG. 43 using colorinformation of VXL 1 and VXL 2 shown in FIG. 39 . For example,three-dimensional data encoding device 1300 calculates an average value,a median, a weighted average value, or the like of the color informationof VXL 1 and VXL 2 as the color information of VXL 12. In this manner,three-dimensional data encoding device 1300 may control a reduction ofthe amount of data by changing the depth of the octree.

Three-dimensional data encoding device 1300 may set the depthinformation of the octree to units of worlds, units of spaces, or unitsof volumes. In this case, three-dimensional data encoding device 1300may append the depth information to header information of the world,header information of the space, or header information of the volume. Inall worlds, spaces, and volumes associated with different times, thesame value may be used as the depth information. In this case,three-dimensional data encoding device 1300 may append the depthinformation to header information managing the worlds associated withall times.

When the color information is included in the voxels, transformer 1303applies frequency transformation, e.g. orthogonal transformation, to aprediction residual of the color information of the voxels in thevolume. For example, transformer 1303 creates a one-dimensional array byscanning the prediction residual in a certain scan order. Subsequently,transformer 1303 transforms the one-dimensional array to a frequencydomain by applying one-dimensional orthogonal transformation to thecreated one-dimensional array. With this, when a value of the predictionresidual in the volume is similar, a value of a low-frequency componentincreases and a value of a high-frequency component decreases. As such,it is possible to more efficiently reduce an code amount in quantizer1304.

Transformer 1303 does not need to use orthogonal transformation in onedimension, but may also use orthogonal transformation in two or moredimensions. For example, transformer 1303 maps the prediction residualto a two-dimensional array in a certain scan order, and appliestwo-dimensional orthogonal transformation to the obtainedtwo-dimensional array. Transformer 1303 may select an orthogonaltransformation method to be used from a plurality of orthogonaltransformation methods. In this case, three-dimensional data encodingdevice 1300 appends, to the bitstream, information indicating whichorthogonal transformation method is used. Transformer 1303 may select anorthogonal transformation method to be used from a plurality oforthogonal transformation methods in different dimensions. In this case,three-dimensional data encoding device 1300 appends, to the bitstream,in how many dimensions the orthogonal transformation method is used.

For example, transformer 1303 matches the scan order of the predictionresidual to a scan order (breadth-first, depth-first, or the like) inthe octree in the volume. This makes it possible to reduce overhead,since information indicating the scan order of the prediction residualdoes not need to be appended to the bitstream. Transformer 1303 mayapply a scan order different from the scan order of the octree. In thiscase, three-dimensional data encoding device 1300 appends, to thebitstream, information indicating the scan order of the predictionresidual. This enables three-dimensional data encoding device 1300 toefficiently encode the prediction residual. Three-dimensional dataencoding device 1300 may append, to the bitstream, information (flag,etc.) indicating whether to apply the scan order of the octree, and mayalso append, to the bitstream, information indicating the scan order ofthe prediction residual when the scan order of the octree is notapplied.

Transformer 1303 does not only transform the prediction residual of thecolor information, and may also transform other attribute informationincluded in the voxels. For example, transformer 1303 may transform andencode information, such as reflectance information, obtained whenobtaining a point cloud through LiDAR and the like.

Transformer 1303 may skip these processes when the spaces do not includeattribute information such as color information. Three-dimensional dataencoding device 1300 may append, to the bitstream, information (flag)indicating whether to skip the processes of transformer 1303.

Quantizer 1304 generates a quantized coefficient by performingquantization using a quantization control parameter on a frequencycomponent of the prediction residual generated by transformer 1303. Withthis, the amount of information is further reduced. The generatedquantized coefficient is outputted to entropy encoder 1313. Quantizer1304 may control the quantization control parameter in units of worlds,units of spaces, or units of volumes. In this case, three-dimensionaldata encoding device 1300 appends the quantization control parameter toeach header information and the like. Quantizer 1304 may performquantization control by changing a weight per frequency component of theprediction residual. For example, quantizer 1304 may precisely quantizea low-frequency component and roughly quantize a high-frequencycomponent. In this case, three-dimensional data encoding device 1300 mayappend, to a header, a parameter expressing a weight of each frequencycomponent.

Quantizer 1304 may skip these processes when the spaces do not includeattribute information such as color information. Three-dimensional dataencoding device 1300 may append, to the bitstream, information (flag)indicating whether to skip the processes of quantizer 1304.

Inverse quantizer 1305 generates an inverse quantized coefficient of theprediction residual by performing inverse quantization on the quantizedcoefficient generated by quantizer 1304 using the quantization controlparameter, and outputs the generated inverse quantized coefficient toinverse transformer 1306.

Inverse transformer 1306 generates an inverse transformation-appliedprediction residual by applying inverse transformation on the inversequantized coefficient generated by inverse quantizer 1305. This inversetransformation-applied prediction residual does not need to completelycoincide with the prediction residual outputted by transformer 1303,since the inverse transformation-applied prediction residual is aprediction residual that is generated after the quantization.

Adder 1307 adds, to generate a reconstructed volume, (i) the inversetransformation-applied prediction residual generated by inversetransformer 1306 to (ii) a predicted volume that is generated throughintra prediction or intra prediction, which will be described later, andis used to generate a pre-quantized prediction residual. Thisreconstructed volume is stored in reference volume memory 1308 orreference space memory 1310.

Intra predictor 1309 generates a predicted volume of an encoding targetvolume using attribute information of a neighboring volume stored inreference volume memory 1308. The attribute information includes colorinformation or a reflectance of the voxels. Intra predictor 1309generates a predicted value of color information or a reflectance of theencoding target volume.

FIG. 44 is a diagram for describing an operation of intra predictor1309. For example, intra predictor 1309 generates the predicted volumeof the encoding target volume (volume idx=3) shown in FIG. 44 , using aneighboring volume (volume idx=0). Volume idx here is identifierinformation that is appended to a volume in a space, and a differentvalue is assigned to each volume. An order of assigning volume idx maybe the same as an encoding order, and may also be different from theencoding order. For example, intra predictor 1309 uses an average valueof color information of voxels included in volume idx=0, which is aneighboring volume, as the predicted value of the color information ofthe encoding target volume shown in FIG. 44 . In this case, a predictionresidual is generated by deducting the predicted value of the colorinformation from the color information of each voxel included in theencoding target volume. The following processes are performed bytransformer 1303 and subsequent processors with respect to thisprediction residual. In this case, three-dimensional data encodingdevice 1300 appends, to the bitstream, neighboring volume informationand prediction mode information. The neighboring volume information hereis information indicating a neighboring volume used in the prediction,and indicates, for example, volume idx of the neighboring volume used inthe prediction. The prediction mode information here indicates a modeused to generate the predicted volume. The mode is, for example, anaverage value mode in which the predicted value is generated using anaverage value of the voxels in the neighboring volume, or a median modein which the predicted value is generated using the median of the voxelsin the neighboring volume.

Intra predictor 1309 may generate the predicted volume using a pluralityof neighboring volumes. For example, in the structure shown in FIG. 44 ,intra predictor 1309 generates predicted volume 0 using a volume withvolume idx=0, and generates predicted volume 1 using a volume withvolume idx=1. Intra predictor 1309 then generates an average ofpredicted volume 0 and predicted volume 1 as a final predicted volume.In this case, three-dimensional data encoding device 1300 may append, tothe bitstream, a plurality of volumes idx of a plurality of volumes usedto generate the predicted volume.

FIG. 45 is a diagram schematically showing the inter prediction processaccording to the present embodiment. Inter predictor 1311 encodes (interpredicts) a space (SPC) associated with certain time T_Cur using anencoded space associated with different time T_LX. In this case, interpredictor 1311 performs an encoding process by applying a rotation andtranslation process to the encoded space associated with different timeT_LX.

Three-dimensional data encoding device 1300 appends, to the bitstream,RT information relating to a rotation and translation process suited tothe space associated with different time T_LX. Different time T_LX is,for example, time T_L0 before certain time T_Cur. At this point,three-dimensional data encoding device 1300 may append, to thebitstream, RT information RT_L0 relating to a rotation and translationprocess suited to a space associated with time T_L0.

Alternatively, different time T_LX is, for example, time T_L1 aftercertain time T_Cur. At this point, three-dimensional data encodingdevice 1300 may append, to the bitstream, RT information RT_L1 relatingto a rotation and translation process suited to a space associated withtime T_L1.

Alternatively, inter predictor 1311 encodes (bidirectional prediction)with reference to the spaces associated with time T_L0 and time T_L1that differ from each other. In this case, three-dimensional dataencoding device 1300 may append, to the bitstream, both RT informationRT_L0 and RT information RT_L1 relating to the rotation and translationprocess suited to the spaces thereof.

Note that T_L0 has been described as being before T_Cur and T_L1 asbeing after T_Cur, but are not necessarily limited thereto. For example,T_L0 and T_L1 may both be before T_Cur. T_L0 and T_L1 may also both beafter T_Cur.

Three-dimensional data encoding device 1300 may append, to thebitstream, RT information relating to a rotation and translation processsuited to spaces associated with different times, when encoding withreference to each of the spaces. For example, three-dimensional dataencoding device 1300 manages a plurality of encoded spaces to bereferred to, using two reference lists (list L0 and list L1). When afirst reference space in list L0 is L0R0, a second reference space inlist L0 is L0R1, a first reference space in list L1 is L1R0, and asecond reference space in list L1 is L1R1, three-dimensional dataencoding device 1300 appends, to the bitstream, RT information RT_L0R0of L0R0, RT information RT_L0R1 of L0R1, RT information RT_L1R0 of L1R0,and RT information RT_L1R1 of L1R1. For example, three-dimensional dataencoding device 1300 appends these pieces of RT information to a headerand the like of the bitstream.

Three-dimensional data encoding device 1300 determines whether to applyrotation and translation per reference space, when encoding withreference to reference spaces associated with different times. In thiscase, three-dimensional data encoding device 1300 may append, to headerinformation and the like of the bitstream, information (RT flag, etc.)indicating whether rotation and translation are applied per referencespace. For example, three-dimensional data encoding device 1300calculates the RT information and an Iterative Closest Point (ICP) errorvalue, using an ICP algorithm per reference space to be referred to fromthe encoding target space. Three-dimensional data encoding device 1300determines that rotation and translation do not need to be performed andsets the RT flag to OFF, when the ICP error value is lower than or equalto a predetermined fixed value. In contrast, three-dimensional dataencoding device 1300 sets the RT flag to ON and appends the RTinformation to the bitstream, when the ICP error value exceeds the abovefixed value.

FIG. 46 is a diagram showing an example syntax to be appended to aheader of the RT information and the RT flag. Note that a bit countassigned to each syntax may be decided based on a range of this syntax.For example, when eight reference spaces are included in reference listL0, 3 bits may be assigned to MaxRefSpc_l0. The bit count to be assignedmay be variable in accordance with a value each syntax can be, and mayalso be fixed regardless of the value each syntax can be. When the bitcount to be assigned is fixed, three-dimensional data encoding device1300 may append this fixed bit count to other header information.

MaxRefSpc_l0 shown in FIG. 46 indicates a number of reference spacesincluded in reference list L0. RT_flag_l0[i] is an RT flag of referencespace i in reference list L0. When RT_flag_l0[i] is 1, rotation andtranslation are applied to reference space i. When RT_flag_l0[i] is 0,rotation and translation are not applied to reference space i.

R_l0[i] and T_l0[i] are RT information of reference space i in referencelist L0. R_l0[i] is rotation information of reference space i inreference list L0. The rotation information indicates contents of theapplied rotation process, and is, for example, a rotation matrix or aquaternion. T_l0[i] is translation information of reference space i inreference list L0. The translation information indicates contents of theapplied translation process, and is, for example, a translation vector.

MaxRefSpc_l1 indicates a number of reference spaces included inreference list L1. RT_flag_l1[i] is an RT flag of reference space i inreference list L1. When RT_flag_l1[i] is 1, rotation and translation areapplied to reference space i. When RT_flag_l1[i] is 0, rotation andtranslation are not applied to reference space i.

R_l1[i] and T_l1[i] are RT information of reference space i in referencelist L1. R_l1[i] is rotation information of reference space i inreference list L1. The rotation information indicates contents of theapplied rotation process, and is, for example, a rotation matrix or aquaternion. T_l1[i] is translation information of reference space i inreference list L1. The translation information indicates contents of theapplied translation process, and is, for example, a translation vector.

Inter predictor 1311 generates the predicted volume of the encodingtarget volume using information on an encoded reference space stored inreference space memory 1310. As stated above, before generating thepredicted volume of the encoding target volume, inter predictor 1311calculates RT information at an encoding target space and a referencespace using an ICP algorithm, in order to approach an overall positionalrelationship between the encoding target space and the reference space.Inter predictor 1311 then obtains reference space B by applying arotation and translation process to the reference space using thecalculated RT information. Subsequently, inter predictor 1311 generatesthe predicted volume of the encoding target volume in the encodingtarget space using information in reference space B. Three-dimensionaldata encoding device 1300 appends, to header information and the like ofthe encoding target space, the RT information used to obtain referencespace B.

In this manner, inter predictor 1311 is capable of improving precisionof the predicted volume by generating the predicted volume using theinformation of the reference space, after approaching the overallpositional relationship between the encoding target space and thereference space, by applying a rotation and translation process to thereference space. It is possible to reduce the code amount since it ispossible to limit the prediction residual. Note that an example has beendescribed in which ICP is performed using the encoding target space andthe reference space, but is not necessarily limited thereto. Forexample, inter predictor 1311 may calculate the RT information byperforming ICP using at least one of (i) an encoding target space inwhich a voxel or point cloud count is pruned, or (ii) a reference spacein which a voxel or point cloud count is pruned, in order to reduce theprocessing amount.

When the ICP error value obtained as a result of the ICP is smaller thana predetermined first threshold, i.e., when for example the positionalrelationship between the encoding target space and the reference spaceis similar, inter predictor 1311 determines that a rotation andtranslation process is not necessary, and the rotation and translationprocess does not need to be performed. In this case, three-dimensionaldata encoding device 1300 may control the overhead by not appending theRT information to the bitstream.

When the ICP error value is greater than a predetermined secondthreshold, inter predictor 1311 determines that a shape change betweenthe spaces is large, and intra prediction may be applied on all volumesof the encoding target space. Hereinafter, spaces to which intraprediction is applied will be referred to as intra spaces. The secondthreshold is greater than the above first threshold. The presentembodiment is not limited to ICP, and any type of method may be used aslong as the method calculates the RT information using two voxel sets ortwo point cloud sets.

When attribute information, e.g. shape or color information, is includedin the three-dimensional data, inter predictor 1311 searches, forexample, a volume whose attribute information, e.g. shape or colorinformation, is the most similar to the encoding target volume in thereference space, as the predicted volume of the encoding target volumein the encoding target space. This reference space is, for example, areference space on which the above rotation and translation process hasbeen performed. Inter predictor 1311 generates the predicted volumeusing the volume (reference volume) obtained through the search. FIG. 47is a diagram for describing a generating operation of the predictedvolume. When encoding the encoding target volume (volume idx=0) shown inFIG. 47 using inter prediction, inter predictor 1311 searches a volumewith a smallest prediction residual, which is the difference between theencoding target volume and the reference volume, while sequentiallyscanning the reference volume in the reference space. Inter predictor1311 selects the volume with the smallest prediction residual as thepredicted volume. The prediction residuals of the encoding target volumeand the predicted volume are encoded through the processes performed bytransformer 1303 and subsequent processors. The prediction residual hereis a difference between the attribute information of the encoding targetvolume and the attribute information of the predicted volume.Three-dimensional data encoding device 1300 appends, to the header andthe like of the bitstream, volume idx of the reference volume in thereference space, as the predicted volume.

In the example shown in FIG. 47 , the reference volume with volume idx=4of reference space L0R0 is selected as the predicted volume of theencoding target volume. The prediction residuals of the encoding targetvolume and the reference volume, and reference volume idx=4 are thenencoded and appended to the bitstream.

Note that an example has been described in which the predicted volume ofthe attribute information is generated, but the same process may beapplied to the predicted volume of the position information.

Prediction controller 1312 controls whether to encode the encodingtarget volume using intra prediction or inter prediction. A modeincluding intra prediction and inter prediction is referred to here as aprediction mode. For example, prediction controller 1312 calculates theprediction residual when the encoding target volume is predicted usingintra prediction and the prediction residual when the encoding targetvolume is predicted using inter prediction as evaluation values, andselects the prediction mode whose evaluation value is smaller. Note thatprediction controller 1312 may calculate an actual code amount byapplying orthogonal transformation, quantization, and entropy encodingto the prediction residual of the intra prediction and the predictionresidual of the inter prediction, and select a prediction mode using thecalculated code amount as the evaluation value. Overhead information(reference volume idx information, etc.) aside from the predictionresidual may be added to the evaluation value. Prediction controller1312 may continuously select intra prediction when it has been decidedin advance to encode the encoding target space using intra space.

Entropy encoder 1313 generates an encoded signal (encoded bitstream) byvariable-length encoding the quantized coefficient, which is an inputfrom quantizer 1304. To be specific, entropy encoder 1313, for example,binarizes the quantized coefficient and arithmetically encodes theobtained binary signal.

A three-dimensional data decoding device that decodes the encoded signalgenerated by three-dimensional data encoding device 1300 will bedescribed next. FIG. 48 is a block diagram of three-dimensional datadecoding device 1400 according to the present embodiment. Thisthree-dimensional data decoding device 1400 includes entropy decoder1401, inverse quantizer 1402, inverse transformer 1403, adder 1404,reference volume memory 1405, intra predictor 1406, reference spacememory 1407, inter predictor 1408, and prediction controller 1409.

Entropy decoder 1401 variable-length decodes the encoded signal (encodedbitstream). For example, entropy decoder 1401 generates a binary signalby arithmetically decoding the encoded signal, and generates a quantizedcoefficient using the generated binary signal.

Inverse quantizer 1402 generates an inverse quantized coefficient byinverse quantizing the quantized coefficient inputted from entropydecoder 1401, using a quantization parameter appended to the bitstreamand the like.

Inverse transformer 1403 generates a prediction residual by inversetransforming the inverse quantized coefficient inputted from inversequantizer 1402. For example, inverse transformer 1403 generates theprediction residual by inverse orthogonally transforming the inversequantized coefficient, based on information appended to the bitstream.

Adder 1404 adds, to generate a reconstructed volume, (i) the predictionresidual generated by inverse transformer 1403 to (ii) a predictedvolume generated through intra prediction or intra prediction. Thisreconstructed volume is outputted as decoded three-dimensional data andis stored in reference volume memory 1405 or reference space memory1407.

Intra predictor 1406 generates a predicted volume through intraprediction using a reference volume in reference volume memory 1405 andinformation appended to the bitstream. To be specific, intra predictor1406 obtains neighboring volume information (e.g. volume idx) appendedto the bitstream and prediction mode information, and generates thepredicted volume through a mode indicated by the prediction modeinformation, using a neighboring volume indicated in the neighboringvolume information. Note that the specifics of these processes are thesame as the above-mentioned processes performed by intra predictor 1309,except for which information appended to the bitstream is used.

Inter predictor 1408 generates a predicted volume through interprediction using a reference space in reference space memory 1407 andinformation appended to the bitstream. To be specific, inter predictor1408 applies a rotation and translation process to the reference spaceusing the RT information per reference space appended to the bitstream,and generates the predicted volume using the rotated and translatedreference space. Note that when an RT flag is present in the bitstreamper reference space, inter predictor 1408 applies a rotation andtranslation process to the reference space in accordance with the RTflag. Note that the specifics of these processes are the same as theabove-mentioned processes performed by inter predictor 1311, except forwhich information appended to the bitstream is used.

Prediction controller 1409 controls whether to decode a decoding targetvolume using intra prediction or inter prediction. For example,prediction controller 1409 selects intra prediction or inter predictionin accordance with information that is appended to the bitstream andindicates the prediction mode to be used. Note that predictioncontroller 1409 may continuously select intra prediction when it hasbeen decided in advance to decode the decoding target space using intraspace.

Hereinafter, variations of the present embodiment will be described. Inthe present embodiment, an example has been described in which rotationand translation is applied in units of spaces, but rotation andtranslation may also be applied in smaller units. For example,three-dimensional data encoding device 1300 may divide a space intosubspaces, and apply rotation and translation in units of subspaces. Inthis case, three-dimensional data encoding device 1300 generates RTinformation per subspace, and appends the generated RT information to aheader and the like of the bitstream. Three-dimensional data encodingdevice 1300 may apply rotation and translation in units of volumes,which is an encoding unit. In this case, three-dimensional data encodingdevice 1300 generates RT information in units of encoded volumes, andappends the generated RT information to a header and the like of thebitstream. The above may also be combined. In other words,three-dimensional data encoding device 1300 may apply rotation andtranslation in large units and subsequently apply rotation andtranslation in small units. For example, three-dimensional data encodingdevice 1300 may apply rotation and translation in units of spaces, andmay also apply different rotations and translations to each of aplurality of volumes included in the obtained spaces.

In the present embodiment, an example has been described in whichrotation and translation is applied to the reference space, but is notnecessarily limited thereto. For example, three-dimensional dataencoding device 1300 may apply a scaling process and change a size ofthe three-dimensional data. Three-dimensional data encoding device 1300may also apply one or two of the rotation, translation, and scaling.When applying the processes in multiple stages and different units asstated above, a type of the processes applied in each unit may differ.For example, rotation and translation may be applied in units of spaces,and translation may be applied in units of volumes.

Note that these variations are also applicable to three-dimensional datadecoding device 1400.

As stated above, three-dimensional data encoding device 1300 accordingto the present embodiment performs the following processes. FIG. 48 is aflowchart of the inter prediction process performed by three-dimensionaldata encoding device 1300.

Three-dimensional data encoding device 1300 generates predicted positioninformation (e.g. predicted volume) using position information onthree-dimensional points included in three-dimensional reference data(e.g. reference space) associated with a time different from a timeassociated with current three-dimensional data (e.g. encoding targetspace) (S1301). To be specific, three-dimensional data encoding device1300 generates the predicted position information by applying a rotationand translation process to the position information on thethree-dimensional points included in the three-dimensional referencedata.

Note that three-dimensional data encoding device 1300 may perform arotation and translation process using a first unit (e.g. spaces), andmay perform the generating of the predicted position information using asecond unit (e.g. volumes) that is smaller than the first unit. Forexample, three-dimensional data encoding device 1300 searches a volumeamong a plurality of volumes included in the rotated and translatedreference space, whose position information differs the least from theposition information of the encoding target volume included in theencoding target space. Note that three-dimensional data encoding device1300 may perform the rotation and translation process, and thegenerating of the predicted position information in the same unit.

Three-dimensional data encoding device 1300 may generate the predictedposition information by applying (i) a first rotation and translationprocess to the position information on the three-dimensional pointsincluded in the three-dimensional reference data, and (ii) a secondrotation and translation process to the position information on thethree-dimensional points obtained through the first rotation andtranslation process, the first rotation and translation process using afirst unit (e.g. spaces) and the second rotation and translation processusing a second unit (e.g. volumes) that is smaller than the first unit.

For example, as illustrated in FIG. 41 , the position information on thethree-dimensional points and the predicted position information isrepresented using an octree structure. For example, the positioninformation on the three-dimensional points and the predicted positioninformation is expressed in a scan order that prioritizes a breadth overa depth in the octree structure. For example, the position informationon the three-dimensional points and the predicted position informationis expressed in a scan order that prioritizes a depth over a breadth inthe octree structure.

As illustrated in FIG. 46 , three-dimensional data encoding device 1300encodes an RT flag that indicates whether to apply the rotation andtranslation process to the position information on the three-dimensionalpoints included in the three-dimensional reference data. In other words,three-dimensional data encoding device 1300 generates the encoded signal(encoded bitstream) including the RT flag. Three-dimensional dataencoding device 1300 encodes RT information that indicates contents ofthe rotation and translation process. In other words, three-dimensionaldata encoding device 1300 generates the encoded signal (encodedbitstream) including the RT information. Note that three-dimensionaldata encoding device 1300 may encode the RT information when the RT flagindicates to apply the rotation and translation process, and does notneed to encode the RT information when the RT flag indicates not toapply the rotation and translation process.

The three-dimensional data includes, for example, the positioninformation on the three-dimensional points and the attributeinformation (color information, etc.) of each three-dimensional point.Three-dimensional data encoding device 1300 generates predictedattribute information using the attribute information of thethree-dimensional points included in the three-dimensional referencedata (S1302).

Three-dimensional data encoding device 1300 next encodes the positioninformation on the three-dimensional points included in the currentthree-dimensional data, using the predicted position information. Forexample, as illustrated in FIG. 38 , three-dimensional data encodingdevice 1300 calculates differential position information, thedifferential position information being a difference between thepredicted position information and the position information on thethree-dimensional points included in the current three-dimensional data(S1303).

Three-dimensional data encoding device 1300 encodes the attributeinformation of the three-dimensional points included in the currentthree-dimensional data, using the predicted attribute information. Forexample, three-dimensional data encoding device 1300 calculatesdifferential attribute information, the differential attributeinformation being a difference between the predicted attributeinformation and the attribute information on the three-dimensionalpoints included in the current three-dimensional data (S1304).Three-dimensional data encoding device 1300 next performs transformationand quantization on the calculated differential attribute information(S1305).

Lastly, three-dimensional data encoding device 1300 encodes (e.g.entropy encodes) the differential position information and the quantizeddifferential attribute information (S1036). In other words,three-dimensional data encoding device 1300 generates the encoded signal(encoded bitstream) including the differential position information andthe differential attribute information.

Note that when the attribute information is not included in thethree-dimensional data, three-dimensional data encoding device 1300 doesnot need to perform steps S1302, S1304, and S1305. Three-dimensionaldata encoding device 1300 may also perform only one of the encoding ofthe position information on the three-dimensional points and theencoding of the attribute information of the three-dimensional points.

An order of the processes shown in FIG. 49 is merely an example and isnot limited thereto. For example, since the processes with respect tothe position information (S1301 and S1303) and the processes withrespect to the attribute information (S1302, S1304, and S1305) areseparate from one another, they may be performed in an order of choice,and a portion thereof may also be performed in parallel.

With the above, three-dimensional data encoding device 1300 according tothe present embodiment generates predicted position information usingposition information on three-dimensional points included inthree-dimensional reference data associated with a time different from atime associated with current three-dimensional data; and encodesdifferential position information, which is a difference between thepredicted position information and the position information on thethree-dimensional points included in the current three-dimensional data.This makes it possible to improve encoding efficiency since it ispossible to reduce the amount of data of the encoded signal.

Three-dimensional data encoding device 1300 according to the presentembodiment generates predicted attribute information using attributeinformation on three-dimensional points included in three-dimensionalreference data; and encodes differential attribute information, which isa difference between the predicted attribute information and theattribute information on the three-dimensional points included in thecurrent three-dimensional data. This makes it possible to improveencoding efficiency since it is possible to reduce the amount of data ofthe encoded signal.

For example, three-dimensional data encoding device 1300 includes aprocessor and memory. The processor uses the memory to perform the aboveprocesses.

FIG. 48 is a flowchart of the inter prediction process performed bythree-dimensional data decoding device 1400.

Three-dimensional data decoding device 1400 decodes (e.g. entropydecodes) the differential position information and the differentialattribute information from the encoded signal (encoded bitstream)(S1401).

Three-dimensional data decoding device 1400 decodes, from the encodedsignal, an RT flag that indicates whether to apply the rotation andtranslation process to the position information on the three-dimensionalpoints included in the three-dimensional reference data.Three-dimensional data decoding device 1400 encodes RT information thatindicates contents of the rotation and translation process. Note thatthree-dimensional data decoding device 1400 may decode the RTinformation when the RT flag indicates to apply the rotation andtranslation process, and does not need to decode the RT information whenthe RT flag indicates not to apply the rotation and translation process.

Three-dimensional data decoding device 1400 next performs inversetransformation and inverse quantization on the decoded differentialattribute information (S1402).

Three-dimensional data decoding device 1400 next generates predictedposition information (e.g. predicted volume) using the positioninformation on the three-dimensional points included in thethree-dimensional reference data (e.g. reference space) associated witha time different from a time associated with the currentthree-dimensional data (e.g. decoding target space) (S1403). To bespecific, three-dimensional data decoding device 1400 generates thepredicted position information by applying a rotation and translationprocess to the position information on the three-dimensional pointsincluded in the three-dimensional reference data.

More specifically, when the RT flag indicates to apply the rotation andtranslation process, three-dimensional data decoding device 1400 appliesthe rotation and translation process on the position information on thethree-dimensional points included in the three-dimensional referencedata indicated in the RT information. In contrast, when the RT flagindicates not to apply the rotation and translation process,three-dimensional data decoding device 1400 does not apply the rotationand translation process on the position information on thethree-dimensional points included in the three-dimensional referencedata.

Note that three-dimensional data decoding device 1400 may perform therotation and translation process using a first unit (e.g. spaces), andmay perform the generating of the predicted position information using asecond unit (e.g. volumes) that is smaller than the first unit. Notethat three-dimensional data decoding device 1400 may perform therotation and translation process, and the generating of the predictedposition information in the same unit.

Three-dimensional data decoding device 1400 may generate the predictedposition information by applying (i) a first rotation and translationprocess to the position information on the three-dimensional pointsincluded in the three-dimensional reference data, and (ii) a secondrotation and translation process to the position information on thethree-dimensional points obtained through the first rotation andtranslation process, the first rotation and translation process using afirst unit (e.g. spaces) and the second rotation and translation processusing a second unit (e.g. volumes) that is smaller than the first unit.

For example, as illustrated in FIG. 41 , the position information on thethree-dimensional points and the predicted position information isrepresented using an octree structure. For example, the positioninformation on the three-dimensional points and the predicted positioninformation is expressed in a scan order that prioritizes a breadth overa depth in the octree structure. For example, the position informationon the three-dimensional points and the predicted position informationis expressed in a scan order that prioritizes a depth over a breadth inthe octree structure.

Three-dimensional data decoding device 1400 generates predictedattribute information using the attribute information of thethree-dimensional points included in the three-dimensional referencedata (S1404).

Three-dimensional data decoding device 1400 next restores the positioninformation on the three-dimensional points included in the currentthree-dimensional data, by decoding encoded position informationincluded in an encoded signal, using the predicted position information.The encoded position information here is the differential positioninformation. Three-dimensional data decoding device 1400 restores theposition information on the three-dimensional points included in thecurrent three-dimensional data, by adding the differential positioninformation to the predicted position information (S1405).

Three-dimensional data decoding device 1400 restores the attributeinformation of the three-dimensional points included in the currentthree-dimensional data, by decoding encoded attribute informationincluded in an encoded signal, using the predicted attributeinformation. The encoded attribute information here is the differentialposition information. Three-dimensional data decoding device 1400restores the attribute information on the three-dimensional pointsincluded in the current three-dimensional data, by adding thedifferential attribute information to the predicted attributeinformation (S1406).

Note that when the attribute information is not included in thethree-dimensional data, three-dimensional data decoding device 1400 doesnot need to perform steps S1402, S1404, and S1406. Three-dimensionaldata decoding device 1400 may also perform only one of the decoding ofthe position information on the three-dimensional points and thedecoding of the attribute information of the three-dimensional points.

An order of the processes shown in FIG. 50 is merely an example and isnot limited thereto. For example, since the processes with respect tothe position information (S1403 and S1405) and the processes withrespect to the attribute information (S1402, S1404, and S1406) areseparate from one another, they may be performed in an order of choice,and a portion thereof may also be performed in parallel.

Embodiment 8

Information of a three-dimensional point cloud includes geometryinformation (geometry) and attribute information (attribute). Geometryinformation includes coordinates (x-coordinate, y-coordinate,z-coordinate) with respect to a certain point. When geometry informationis encoded, a method of representing the position of each ofthree-dimensional points in octree representation and encoding theoctree information to reduce a code amount is used instead of directlyencoding the coordinates of the three-dimensional point.

On the other hand, attribute information includes informationindicating, for example, color information (RGB, YUV, etc.) of eachthree-dimensional point, a reflectance, and a normal vector. Forexample, a three-dimensional data encoding device is capable of encodingattribute information using an encoding method different from a methodused to encode geometry information.

In the present embodiment, a method of encoding attribute information isexplained. It is to be noted that, in the present embodiment, the methodis explained based on an example case using integer values as values ofattribute information. For example, when each of RGB or YUV colorcomponents is of an 8-bit accuracy, the color component is an integervalue in a range from 0 to 255. When a reflectance value is of 10-bitaccuracy, the reflectance value is an integer in a range from 0 to 1023.It is to be noted that, when the bit accuracy of attribute informationis a decimal accuracy, the three-dimensional data encoding device maymultiply the value by a scale value to round it to an integer value sothat the value of the attribute information becomes an integer value. Itis to be noted that the three-dimensional data encoding device may addthe scale value to, for example, a header of a bitstream.

As a method of encoding attribute information of a three-dimensionalpoint, it is conceivable to calculate a predicted value of the attributeinformation of the three-dimensional point and encode a difference(prediction residual) between the original value of the attributeinformation and the predicted value. For example, when the value ofattribute information at three-dimensional point p is Ap and a predictedvalue is Pp, the three-dimensional data encoding device encodesdifferential absolute value Diffp=|Ap−Pp|. In this case, whenhighly-accurate predicted value Pp can be generated, differentialabsolute value Diffp is small. Thus, for example, it is possible toreduce the code amount by entropy encoding differential absolute valueDiffp using a coding table that reduces an occurrence bit count morewhen differential absolute value Diffp is smaller.

As a method of generating a prediction value of attribute information,it is conceivable to use attribute information of a referencethree-dimensional point that is another three-dimensional point whichneighbors a current three-dimensional point to be encoded. Here, areference three-dimensional point is a three-dimensional point in arange of a predetermined distance from the current three-dimensionalpoint. For example, when there are current three-dimensional pointp=(x1, y1, z1) and three-dimensional point q=(x2, y2, z2), thethree-dimensional data encoding device calculates Euclidean distance d(p, q) between three-dimensional point p and three-dimensional point qrepresented by (Equation A1).d(p,q)=√{square root over ((x1−y1)²+(x2−y2)²+(x3−y3)²)}  (Equation A1)

The three-dimensional data encoding device determines that the positionof three-dimensional point q is closer to the position of currentthree-dimensional point p when Euclidean distance d (p, q) is smallerthan predetermined threshold value THd, and determines to use the valueof the attribute information of three-dimensional point q to generate apredicted value of the attribute information of currentthree-dimensional point p. It is to be noted that the method ofcalculating the distance may be another method, and a Mahalanobisdistance or the like may be used. In addition, the three-dimensionaldata encoding device may determine not to use, in prediction processing,any three-dimensional point outside the predetermined range of distancefrom the current three-dimensional point. For example, whenthree-dimensional point r is present, and distance d (p, r) betweencurrent three-dimensional point p and three-dimensional point r islarger than or equal to threshold value THd, the three-dimensional dataencoding device may determine not to use three-dimensional point r forprediction. It is to be noted that the three-dimensional data encodingdevice may add the information indicating threshold value THd to, forexample, a header of a bitstream;

FIG. 51 is a diagram illustrating an example of three-dimensionalpoints. In this example, distance d (p, q) between currentthree-dimensional point p and three-dimensional point q is smaller thanthreshold value THd. Thus, the three-dimensional data encoding devicedetermines that three-dimensional point q is a referencethree-dimensional point of current three-dimensional point p, anddetermines to use the value of attribute information Aq ofthree-dimensional point q to generate predicted value Pp of attributeinformation Ap of current three-dimensional point p.

In contrast, distance d (p, r) between current three-dimensional point pand three-dimensional point r is larger than or equal to threshold valueTHd. Thus, the three-dimensional data encoding device determines thatthree-dimensional point r is not any reference three-dimensional pointof current three-dimensional point p, and determines not to use thevalue of attribute information Ar of three-dimensional point r togenerate predicted value Pp of attribute information Ap of currentthree-dimensional point p.

In addition, when encoding the attribute information of the currentthree-dimensional point using a predicted value, the three-dimensionaldata encoding device uses a three-dimensional point whose attributeinformation has already been encoded and decoded, as a referencethree-dimensional point. Likewise, when decoding the attributeinformation of a current three-dimensional point to be decoded, thethree-dimensional data decoding device uses a three-dimensional pointwhose attribute information has already been decoded, as a referencethree-dimensional point. In this way, it is possible to generate thesame predicted value at the time of encoding and decoding. Thus, abitstream of the three-dimensional point generated by the encoding canbe decoded correctly at the decoding side.

Furthermore, when encoding attribute information of each ofthree-dimensional points, it is conceivable to classify thethree-dimensional point into one of a plurality of layers using geometryinformation of the three-dimensional point and then encode the attributeinformation. Here, each of the layers classified is referred to as aLevel of Detail (LoD). A method of generating LoDs is explained withreference to FIG. 52 .

First, the three-dimensional data encoding device selects initial pointa0 and assigns initial point a0 to LoD0. Next, the three-dimensionaldata encoding device extracts point a1 distant from point a0 more thanthreshold value Thres_LoD[0] of LoD0 and assigns point a1 to LoD0. Next,the three-dimensional data encoding device extracts point a2 distantfrom point a1 more than threshold value Thres_LoD[0] of LoD0 and assignspoint a2 to LoD0. In this way, the three-dimensional data encodingdevice configures LoD0 in such a manner that the distance between thepoints in LoD0 is larger than threshold value Thres_LoD[0].

Next, the three-dimensional data encoding device selects point b0 whichhas not yet been assigned to any LoD and assigns point b0 to LoD1. Next,the three-dimensional data encoding device extracts point b1 which isdistant from point b0 more than threshold value Thres_LoD[1] of LoD1 andwhich has not yet been assigned to any LoD, and assigns point b1 toLoD1. Next, the three-dimensional data encoding device extracts point b2which is distant from point b1 more than threshold value Thres_LoD[1] ofLoD1 and which has not yet been assigned to any LoD, and assigns pointb2 to LoD1. In this way, the three-dimensional data encoding deviceconfigures LoD1 in such a manner that the distance between the points inLoD1 is larger than threshold value Thres_LoD[1].

Next, the three-dimensional data encoding device selects point c0 whichhas not yet been assigned to any LoD and assigns point c0 to LoD2. Next,the three-dimensional data encoding device extracts point c1 which isdistant from point c0 more than threshold value Thres_LoD[2] of LoD2 andwhich has not yet been assigned to any LoD, and assigns point c1 toLoD2. Next, the three-dimensional data encoding device extracts point c2which is distant from point c1 more than threshold value Thres_LoD[2] ofLoD2 and which has not yet been assigned to any LoD, and assigns pointc2 to LoD2. In this way, the three-dimensional data encoding deviceconfigures LoD2 in such a manner that the distance between the points inLoD2 is larger than threshold value Thres_LoD[2]. For example, asillustrated in FIG. 53 , threshold values Thres_LoD[0], Thres_LoD[1],and Thres_LoD[2] of respective LoDs are set.

In addition, the three-dimensional data encoding device may add theinformation indicating the threshold value of each LoD to, for example,a header of a bitstream. For example, in the case of the exampleillustrated in FIG. 53 , the three-dimensional data encoding device mayadd threshold values Thres_LoD[0], Thres_LoD[1], and Thres_LoD[2] ofrespective LoDs to a header.

Alternatively, the three-dimensional data encoding device may assign allthree-dimensional points which have not yet been assigned to any LoD inthe lowermost-layer LoD. In this case, the three-dimensional dataencoding device is capable of reducing the code amount of the header bynot assigning the threshold value of the lowermost-layer LoD to theheader. For example, in the case of the example illustrated in FIG. 53 ,the three-dimensional data encoding device assigns threshold valuesThres_LoD[0] and Thres_LoD[1] to the header, and does not assignThres_LoD[2] to the header. In this case, the three-dimensional dataencoding device may estimate value 0 of Thres_LoD[2]. In addition, thethree-dimensional data encoding device may add the number of LoDs to aheader. In this way, the three-dimensional data encoding device iscapable of determining the lowermost-layer LoD using the number of LoDs.

In addition, setting threshold values for the respective layers LoDs insuch a manner that a larger threshold value is set to a higher layermakes a higher layer (layer closer to LoD0) to have a sparse point cloud(sparse) in which three-dimensional points are more distant and makes alower layer to have a dense point cloud (dense) in whichthree-dimensional points are closer. It is to be noted that, in anexample illustrated in FIG. 53 , LoD0 is the uppermost layer.

In addition, the method of selecting an initial three-dimensional pointat the time of setting each LoD may depend on an encoding order at thetime of geometry information encoding. For example, thethree-dimensional data encoding device configures LoD0 by selecting thethree-dimensional point encoded first at the time of the geometryinformation encoding as initial point a0 of LoD0, and selecting point a1and point a2 from initial point a0 as the origin. The three-dimensionaldata encoding device then may select the three-dimensional point whosegeometry information has been encoded at the earliest time amongthree-dimensional points which do not belong to LoD0, as initial pointb0 of LoD1. In other words, the three-dimensional data encoding devicemay select the three-dimensional point whose geometry information hasbeen encoded at the earliest time among three-dimensional points whichdo not belong to layers (LoD0 to LoDn−1) above LoDn, as initial point n0of LoDn. In this way, the three-dimensional data encoding device iscapable of configuring the same LoD as in encoding by using, indecoding, the initial point selecting method similar to the one used inthe encoding, which enables appropriate decoding of a bitstream. Morespecifically, the three-dimensional data encoding device selects thethree-dimensional point whose geometry information has been decoded atthe earliest time among three-dimensional points which do not belong tolayers above LoDn, as initial point n0 of LoDn.

Hereinafter, a description is given of a method of generating thepredicted value of the attribute information of each three-dimensionalpoint using information of LoDs. For example, when encodingthree-dimensional points starting with the three-dimensional pointsincluded in LoD0, the three-dimensional data encoding device generatescurrent three-dimensional points which are included in LoD1 usingencoded and decoded (hereinafter also simply referred to as “encoded”)attribute information included in LoD0 and LoD1. In this way, thethree-dimensional data encoding device generates a predicted value ofattribute information of each three-dimensional point included in LoDnusing encoded attribute information included in LoDn′ (n′≤n). In otherwords, the three-dimensional data encoding device does not use attributeinformation of each of three-dimensional points included in any layerbelow LoDn to calculate a predicted value of attribute information ofeach of the three-dimensional points included in LoDn.

For example, the three-dimensional data encoding device calculates anaverage of attribute information of N or less three dimensional pointsamong encoded three-dimensional points surrounding a currentthree-dimensional point to be encoded, to generate a predicted value ofattribute information of the current three-dimensional point. Inaddition, the three-dimensional data encoding device may add value N to,for example, a header of a bitstream. It is to be noted that thethree-dimensional data encoding device may change value N for eachthree-dimensional point, and may add value N for each three-dimensionalpoint. This enables selection of appropriate N for eachthree-dimensional point, which makes it possible to increase theaccuracy of the predicted value. Accordingly, it is possible to reducethe prediction residual. Alternatively, the three-dimensional dataencoding device may add value N to a header of a bitstream, and may fixthe value indicating N in the bitstream. This eliminates the need toencode or decode value N for each three-dimensional point, which makesit possible to reduce the processing amount. In addition, thethree-dimensional data encoding device may encode the values of Nseparately for each LoD. In this way, it is possible to increase thecoding efficiency by selecting appropriate N for each LoD.

Alternatively, the three-dimensional data encoding device may calculatea predicted value of attribute information of three-dimensional pointbased on weighted average values of attribute information of encoded Nneighbor three-dimensional points. For example, the three-dimensionaldata encoding device calculates weights using distance informationbetween a current three-dimensional point and each of N neighborthree-dimensional points.

When encoding value N for each LoD, for example, the three-dimensionaldata encoding device sets larger value N to a higher layer LoD, and setssmaller value N to a lower layer LoD. The distance betweenthree-dimensional points belonging to a higher layer LoD is large, thereis a possibility that it is possible to increase the prediction accuracyby setting large value N, selecting a plurality of neighborthree-dimensional points, and averaging the values. Furthermore, thedistance between three-dimensional points belonging to a lower layer LoDis small, it is possible to perform efficient prediction while reducingthe processing amount of averaging by setting smaller value N;

FIG. 54 is a diagram illustrating an example of attribute information tobe used for predicted values. As described above, the predicted value ofpoint P included in LoDN is generated using encoded neighbor point P′included in LoDN′ (N′≤N). Here, neighbor point P′ is selected based onthe distance from point P. For example, the predicted value of attributeinformation of point b2 illustrated in FIG. 54 is generated usingattribute information of each of points a0, a1, b0, and b1.

Neighbor points to be selected vary depending on the values of Ndescribed above. For example, in the case of N=5, a0, a1, a2, b0, and b1are selected as neighbor points. In the case of N=4, a0, a1, a2, and b1are selected based on distance information.

The predicted value is calculated by distance-dependent weightedaveraging. For example, in the example illustrated in FIG. 54 ,predicted value a2p of point a2 is calculated by weighted averaging ofattribute information of each of point a0 and a1, as represented by(Equation A2) and (Equation A3). It is to be noted that A_(i) is anattribute information value of ai.

$\begin{matrix}{{a2p} = {\sum\limits_{i = 0}^{1}{w_{i} \times A_{i}}}} & ( {{Equation}{A2}} )\end{matrix}$ $\begin{matrix}{w_{i} = \frac{\frac{1}{d( {{a2},{ai}} )}}{\sum\limits_{j = 0}^{1}\frac{1}{d( {{a2},{aj}} )}}} & ( {{Equation}{A3}} )\end{matrix}$

In addition, predicted value b2p of point b2 is calculated by weightedaveraging of attribute information of each of point a0, a1, a2, b0, andb1, as represented by (Equation A4) and (Equation A6). It is to be notedthat B_(i) is an attribute information value of bi.

$\begin{matrix}{{b2p} = {{\sum\limits_{i = 0}^{2}{{wa}_{i} \times A_{i}}} + {\sum\limits_{i = 0}^{1}{{wb}_{i} \times B_{i}}}}} & ( {{Equation}{A4}} )\end{matrix}$ $\begin{matrix}{{wa}_{i} = \frac{\frac{1}{d( {{b2},{ai}} )}}{{\sum\limits_{j = 0}^{2}\frac{1}{d( {{b2},{aj}} )}} + {\sum\limits_{j = 0}^{1}\frac{1}{d( {{b2},{bj}} )}}}} & ( {{Equation}{A5}} )\end{matrix}$ $\begin{matrix}{{wb}_{i} = \frac{\frac{1}{d( {{b2},{bi}} )}}{{\sum\limits_{j = 0}^{2}\frac{1}{d( {{b2},{aj}} )}} + {\sum\limits_{j = 0}^{1}\frac{1}{d( {{b2},{bj}} )}}}} & ( {{Equation}{A6}} )\end{matrix}$

In addition, the three-dimensional data encoding device may calculate adifference value (prediction residual) generated from the value ofattribute information of a three-dimensional point and neighbor points,and may quantize the calculated prediction residual. For example, thethree-dimensional data encoding device performs quantization by dividingthe prediction residual by a quantization scale (also referred to as aquantization step). In this case, an error (quantization error) whichmay be generated by quantization reduces as the quantization scale issmaller. In the other case where the quantization scale is larger, theresulting quantization error is larger.

It is to be noted that the three-dimensional data encoding device maychange the quantization scale to be used for each LoD. For example, thethree-dimensional data encoding device reduces the quantization scalemore for a higher layer, and increases the quantization scale more for alower layer. The value of attribute information of a three-dimensionalpoint belonging to a higher layer may be used as a predicted value ofattribute information of a three-dimensional point belonging to a lowerlayer. Thus, it is possible to increase the coding efficiency byreducing the quantization scale for the higher layer to reduce thequantization error that can be generated in the higher layer and toincrease the prediction accuracy of the predicted value. It is to benoted that the three-dimensional data encoding device may add thequantization scale to be used for each LoD to, for example, a header. Inthis way, the three-dimensional data encoding device can decode thequantization scale correctly, thereby appropriately decoding thebitstream.

In addition, the three-dimensional data encoding device may convert asigned integer value (signed quantized value) which is a quantizedprediction residual into an unsigned integer value (unsigned quantizedvalue). This eliminates the need to consider occurrence of a negativeinteger when entropy encoding the prediction residual. It is to be notedthat the three-dimensional data encoding device does not always need toconvert a signed integer value into an unsigned integer value, and, forexample, that the three-dimensional data encoding device may entropyencode a sign bit separately.

The prediction residual is calculated by subtracting a prediction valuefrom the original value. For example, as represented by (Equation A7),prediction residual a2r of point a2 is calculated by subtractingpredicted value a2p of point a2 from value A₂ of attribute informationof point a2. As represented by (Equation A8), prediction residual b2r ofpoint b2 is calculated by subtracting predicted value b2p of point b2from value B₂ of attribute information of point b2.a2r=A ₂ −a2p  (Equation A7)b2r=B ₂ −b2p  (Equation A8)

In addition, the prediction residual is quantized by being divided by aQuantization Step (QS). For example, quantized value a2q of point a2 iscalculated according to (Equation A9). Quantized value b2q of point b2is calculated according to (Equation A10). Here, QS_LoD0 is a QS forLoD0, and QS_LoD1 is a QS for LoD1. In other words, a QS may be changedaccording to an LoD.a2q=a2r/QS_LoD0  (Equation A9)b2q=b2r/QS_LoD1  (Equation A10)

In addition, the three-dimensional data encoding device converts signedinteger values which are quantized values as indicated below intounsigned integer values as indicated below. When signed integer valuea2q is smaller than 0, the three-dimensional data encoding device setsunsigned integer value a2u to −1−(2×a2q). When signed integer value a2qis 0 or more, the three-dimensional data encoding device sets unsignedinteger value a2u to 2×a2q.

Likewise, when signed integer value b2q is smaller than 0, thethree-dimensional data encoding device sets unsigned integer value b2uto −1−(2×b2q). When signed integer value b2q is 0 or more, thethree-dimensional data encoding device sets unsigned integer value b2uto 2×b2q.

In addition, the three-dimensional data encoding device may encode thequantized prediction residual (unsigned integer value) by entropyencoding. For example, the three-dimensional data encoding device maybinarize the unsigned integer value and then apply binary arithmeticencoding to the binary value.

It is to be noted that, in this case, the three-dimensional dataencoding device may switch binarization methods according to the valueof a prediction residual. For example, when prediction residual pu issmaller than threshold value R_TH, the three-dimensional data encodingdevice binarizes prediction residual pu using a fixed bit count requiredfor representing threshold value R_TH. In addition, when predictionresidual pu is larger than or equal to threshold value R_TH, thethree-dimensional data encoding device binarizes the binary data ofthreshold value R_TH and the value of (pu−R_TH), usingexponential-Golomb coding, or the like.

For example, when threshold value R_TH is 63 and prediction residual puis smaller than 63, the three-dimensional data encoding device binarizesprediction residual pu using 6 bits. When prediction residual pu islarger than or equal to 63, the three-dimensional data encoding deviceperforms arithmetic encoding by binarizing the binary data (111111) ofthreshold value R_TH and (pu−63) using exponential-Golomb coding.

In a more specific example, when prediction residual pu is 32, thethree-dimensional data encoding device generates 6-bit binary data(100000), and arithmetic encodes the bit sequence. In addition, whenprediction residual pu is 66, the three-dimensional data encoding devicegenerates binary data (111111) of threshold value R_TH and a bitsequence (00100) representing value 3 (66-63) using exponential-Golombcoding, and arithmetic encodes the bit sequence (111111+00100).

In this way, the three-dimensional data encoding device can performencoding while preventing a binary bit count from increasing abruptly inthe case where a prediction residual becomes large by switchingbinarization methods according to the magnitude of the predictionresidual. It is to be noted that the three-dimensional data encodingdevice may add threshold value R_TH to, for example, a header of abitstream.

For example, in the case where encoding is performed at a high bit rate,that is, when a quantization scale is small, a small quantization errorand a high prediction accuracy are obtained. As a result, a predictionresidual may not be large. Thus, in this case, the three-dimensionaldata encoding device sets large threshold value R_TH. This reduces thepossibility that the binary data of threshold value R_TH is encoded,which increases the coding efficiency. In the opposite case whereencoding is performed at a low bit rate, that is, when a quantizationscale is large, a large quantization error and a low prediction accuracyare obtained. As a result, a prediction residual may be large. Thus, inthis case, the three-dimensional data encoding device sets smallthreshold value R_TH. In this way, it is possible to prevent abruptincrease in bit length of binary data.

In addition, the three-dimensional data encoding device may switchthreshold value R_TH for each LoD, and may add threshold value R_TH foreach LoD to, for example, a header. In other words, thethree-dimensional data encoding device may switch binarization methodsfor each LoD. For example, since distances between three-dimensionalpoints are large in a higher layer, a prediction accuracy is low, whichmay increase a prediction residual. Thus, the three-dimensional dataencoding device prevents abrupt increase in bit length of binary data bysetting small threshold value R_TH to the higher layer. In addition,since distances between three-dimensional points are small in a lowerlayer, a prediction accuracy is high, which may reduce a predictionresidual. Thus, the three-dimensional data encoding device increases thecoding efficiency by setting large threshold value R_TH to the lowerlayer;

FIG. 55 is a diagram indicating examples of exponential-Golomb codes.The diagram indicates the relationships between pre-binarization values(non-binary values) and post-binarization bits (codes). It is to benoted that 0 and 1 indicated in FIG. 55 may be inverted.

The three-dimensional data encoding device applies arithmetic encodingto the binary data of prediction residuals. In this way, the codingefficiency can be increased. It is to be noted that, in the applicationof the arithmetic encoding, there is a possibility that occurrenceprobability tendencies of 0 and 1 in each bit vary, in binary data,between an n-bit code which is a part binarized by n bits and aremaining code which is a part binarized using exponential-Golombcoding. Thus, the three-dimensional data encoding device may switchmethods of applying arithmetic encoding between the n-bit code and theremaining code.

For example, the three-dimensional data encoding device performsarithmetic encoding on the n-bit code using one or more coding tables(probability tables) different for each bit. At this time, thethree-dimensional data encoding device may change the number of codingtables to be used for each bit. For example, the three-dimensional dataencoding device performs arithmetic encoding using one coding table forfirst bit b0 in an n-bit code. The three-dimensional data encodingdevice uses two coding tables for the next bit b1. The three-dimensionaldata encoding device switches coding tables to be used for arithmeticencoding of bit b1 according to the value (0 or 1) of b0. Likewise, thethree-dimensional data encoding device uses four coding tables for thenext bit b2. The three-dimensional data encoding device switches codingtables to be used for arithmetic encoding of bit b2 according to thevalues (in a range from 0 to 3) of b0 and b1.

In this way, the three-dimensional data encoding device uses 2n⁻¹ codingtables when arithmetic encoding each bit bn−1 in n-bit code. Thethree-dimensional data encoding device switches coding tables to be usedaccording to the values (occurrence patterns) of bits before bn−1. Inthis way, the three-dimensional data encoding device can use codingtables appropriate for each bit, and thus can increase the codingefficiency.

It is to be noted that the three-dimensional data encoding device mayreduce the number of coding tables to be used for each bit. For example,the three-dimensional data encoding device may switch 2^(m) codingtables according to the values (occurrence patterns) of m bits (m<n−1)before bn−1 when arithmetic encoding each bit bn−1. In this way, it ispossible to increase the coding efficiency while reducing the number ofcoding tables to be used for each bit. It is to be noted that thethree-dimensional data encoding device may update the occurrenceprobabilities of 0 and 1 in each coding table according to the values ofbinary data occurred actually. In addition, the three-dimensional dataencoding device may fix the occurrence probabilities of 0 and 1 incoding tables for some bit(s). In this way, it is possible to reduce thenumber of updates of occurrence probabilities, and thus to reduce theprocessing amount.

For example, when an n-bit code is b0, b1, b2, . . . , bn−1, the codingtable for b0 is one table (CTb0). Coding tables for b1 are two tables(CTb10 and CTb11). Coding tables to be used are switched according tothe value (0 or 1) of b0. Coding tables for b2 are four tables (CTb20,CTb21, CTb22, and CTb23). Coding tables to be used are switchedaccording to the values (in the range from 0 to 3) of b0 and b1. Codingtables for bn−1 are 2^(n-1) tables (CTbn0, CTbn1, . . . , CTbn(2^(n-1)−1)). Coding tables to be used are switched according to thevalues (in a range from 0 to 2^(n-1)−1) of b0, b1, . . . , bn−2.

It is to be noted that the three-dimensional data encoding device mayapply, to an n-bit code, arithmetic encoding (m=2^(n)) by m-ary thatsets the value in the range from 0 to 2^(n)−1 without binarization. Whenthe three-dimensional data encoding device arithmetic encodes an n-bitcode by an m-ary, the three-dimensional data decoding device mayreconstruct the n-bit code by arithmetic decoding the m-ary;

FIG. 56 is a diagram for illustrating processing in the case whereremaining codes are exponential-Golomb codes. As indicated in FIG. 56 ,each remaining code which is a part binarized using exponential-Golombcoding includes a prefix and a suffix. For example, thethree-dimensional data encoding device switches coding tables betweenthe prefix and the suffix. In other words, the three-dimensional dataencoding device arithmetic encodes each of bits included in the prefixusing coding tables for the prefix, and arithmetic encodes each of bitsincluded in the suffix using coding tables for the suffix.

It is to be noted that the three-dimensional data encoding device mayupdate the occurrence probabilities of 0 and 1 in each coding tableaccording to the values of binary data occurred actually. In addition,the three-dimensional data encoding device may fix the occurrenceprobabilities of 0 and 1 in one of coding tables. In this way, it ispossible to reduce the number of updates of occurrence probabilities,and thus to reduce the processing amount. For example, thethree-dimensional data encoding device may update the occurrenceprobabilities for the prefix, and may fix the occurrence probabilitiesfor the suffix.

In addition, the three-dimensional data encoding device decodes aquantized prediction residual by inverse quantization andreconstruction, and uses a decoded value which is the decoded predictionresidual for prediction of a current three-dimensional point to beencoded and the following three-dimensional point(s). More specifically,the three-dimensional data encoding device calculates an inversequantized value by multiplying the quantized prediction residual(quantized value) with a quantization scale, and adds the inversequantized value and a prediction value to obtain the decoded value(reconstructed value).

For example, quantized value a2iq of point a2 is calculated usingquantized value a2q of point a2 according to (Equation A11). Inversequantized value b2iq of point b2q is calculated using quantized valueb2q of point b2 according to (Equation A12). Here, QS_LoD0 is a QS forLoD0, and QS_LoD1 is a QS for LoD1. In other words, a QS may be changedaccording to an LoD.a2iq=a2q×QS_LoD0  (Equation A11)b2iq=b2q×QS_LoD1  (Equation A12)

For example, as represented by (Equation A13), decoded value a2rec ofpoint a2 is calculated by adding inverse quantization value a2iq ofpoint a2 to predicted value a2p of point a2. As represented by (EquationA14), decoded value b2rec of point b2 is calculated by adding inversequantized value b2iq of point b2 to predicted value b2p of point b2.a2rec=a2iq+a2p  (Equation A13)b2rec=b2iq+b2p  (Equation A14)

Hereinafter, a syntax example of a bitstream according to the presentembodiment is described. FIG. 57 is a diagram indicating the syntaxexample of an attribute header (attribute_header) according to thepresent embodiment. The attribute header is header information ofattribute information. As indicated in FIG. 57 , the attribute headerincludes the number of layers information (NumLoD), the number ofthree-dimensional points information (NumOfPoint[i]), a layer thresholdvalue (Thres_LoD[i]), the number of neighbor points information(NumNeighborPoint[i]), a prediction threshold value (THd[i]), aquantization scale (QS[i]), and a binarization threshold value(R_TH[i]).

The number of layers information (NumLoD) indicates the number of LoDsto be used.

The number of three-dimensional points information (NumOfPoint[i])indicates the number of three-dimensional points belonging to layer i.It is to be noted that the three-dimensional data encoding device mayadd, to another header, the number of three-dimensional pointsinformation indicating the total number of three-dimensional points. Inthis case, the three-dimensional data encoding device does not need toadd, to a header, NumOfPoint [NumLoD−1] indicating the number ofthree-dimensional points belonging to the lowermost layer. In this case,the three-dimensional data decoding device is capable of calculatingNumOfPoint [NumLoD−1] according to (Equation A15). In this case, it ispossible to reduce the code amount of the header.

$\begin{matrix}{{{NumOfPoint}\lbrack {{NumLoD} - 1} \rbrack} = {{AllNumOfPoint} - {\sum\limits_{j = 0}^{{NumLoD} - 2}{{NumOfPoint}\lbrack f\rbrack}}}} & ( {{Equation}{A15}} )\end{matrix}$

The layer threshold value (Thres_LoD[i]) is a threshold value to be usedto set layer i. The three-dimensional data encoding device and thethree-dimensional data decoding device configure LoDi in such a mannerthat the distance between points in LoDi becomes larger than thresholdvalue Thres_LoD[i]. The three-dimensional data encoding device does notneed to add the value of Thres_LoD [NumLoD−1] (lowermost layer) to aheader. In this case, the three-dimensional data decoding device mayestimate 0 as the value of Thres_LoD [NumLoD−1]. In this case, it ispossible to reduce the code amount of the header.

The number of neighbor points information (NumNeighborPoint[i])indicates the upper limit value of the number of neighbor points to beused to generate a predicted value of a three-dimensional pointbelonging to layer i. The three-dimensional data encoding device maycalculate a predicted value using the number of neighbor points M whenthe number of neighbor points M is smaller than NumNeighborPoint[i](M<NumNeighborPoint[i]). Furthermore, when there is no need todifferentiate the values of NumNeighborPoint[i] for respective LoDs, thethree-dimensional data encoding device may add a piece of the number ofneighbor points information (NumNeighborPoint) to be used in all LoDs toa header.

The prediction threshold value (THd[i]) indicates the upper limit valueof the distance between a current three-dimensional point to be encodedor decoded in layer i and each of neighbor three-dimensional points tobe used to predict the current three-dimensional point. Thethree-dimensional data encoding device and the three-dimensional datadecoding device do not use, for prediction, any three-dimensional pointdistant from the current three-dimensional point over THd[i]. It is tobe noted that, when there is no need to differentiate the values ofTHd[i] for respective LoDs, the three-dimensional data encoding devicemay add a single prediction threshold value (THd) to be used in all LoDsto a header.

The quantization scale (QS[i]) indicates a quantization scale to be usedfor quantization and inverse quantization in layer i.

The binarization threshold value (R_TH[i]) is a threshold value forswitching binarization methods of prediction residuals ofthree-dimensional points belonging to layer i. For example, thethree-dimensional data encoding device binarizes prediction residual puusing a fixed bit count when a prediction residual is smaller thanthreshold value R_TH, and binarizes the binary data of threshold valueR_TH and the value of (pu−R_TH) using exponential-Golomb coding when aprediction residual is larger than or equal to threshold value R_TH. Itis to be noted that, when there is no need to switch the values ofR_TH[i] between LoDs, the three-dimensional data encoding device may adda single binarization threshold value (R_TH) to be used in all LoDs to aheader.

It is to be noted that R_TH[i] may be the maximum value which can berepresented by n bits. For example, R_TH is 63 in the case of 6 bits,and R_TH is 255 in the case of 8 bits. Alternatively, thethree-dimensional data encoding device may encode a bit count instead ofencoding the maximum value which can be represented by n bits as abinarization threshold value. For example, the three-dimensional dataencoding device may add value 6 in the case of R_TH[i]=63 to a header,and may add value 8 in the case of R_TH[i]=255 to a header.Alternatively, the three-dimensional data encoding device may define theminimum value (minimum bit count) representing R_TH[i], and add arelative bit count from the minimum value to a header. For example, thethree-dimensional data encoding device may add value 0 to a header whenR_TH[i]=63 is satisfied and the minimum bit count is 6, and may addvalue 2 to a header when R_TH[i]=255 is satisfied and the minimum bitcount is 6.

Alternatively, the three-dimensional data encoding device may entropyencode at least one of NumLoD, Thres_LoD[i], NumNeighborPoint[i],THd[i], QS[i], and R_TH[i], and add the entropy encoded one to a header.For example, the three-dimensional data encoding device may binarizeeach value and perform arithmetic encoding on the binary value. Inaddition, the three-dimensional data encoding device may encode eachvalue using a fixed length in order to reduce the processing amount.

Alternatively, the three-dimensional data encoding device does notalways need to add at least one of NumLoD, Thres_LoD[i],NumNeighborPoint[i], THd[i], QS[i], and R_TH[i] to a header. Forexample, at least one of these values may be defined by a profile or alevel in a standard, or the like. In this way, it is possible to reducethe bit amount of the header;

FIG. 58 is a diagram indicating the syntax example of attribute data(attribute_data) according to the present embodiment. The attribute dataincludes encoded data of the attribute information of a plurality ofthree-dimensional points. As indicated in FIG. 58 , the attribute dataincludes an n-bit code and a remaining code.

The n-bit code is encoded data of a prediction residual of a value ofattribute information or a part of the encoded data. The bit length ofthe n-bit code depends on value R_TH[i]. For example, the bit length ofthe n-bit code is 6 bits when the value indicated by R_TH[i] is 63, thebit length of the n-bit code is 8 bits when the value indicated byR_TH[i] is 255.

The remaining code is encoded data encoded using exponential-Golombcoding among encoded data of the prediction residual of the value of theattribute information. The remaining code is encoded or decoded when thevalue of the n-bit code is equal to R_TH[i]. The three-dimensional datadecoding device decodes the prediction residual by adding the value ofthe n-bit code and the value of the remaining code. It is to be notedthat the remaining code does not always need to be encoded or decodedwhen the value of the n-bit code is not equal to R_TH[i].

Hereinafter, a description is given of a flow of processing in thethree-dimensional data encoding device. FIG. 59 is a flowchart of athree-dimensional data encoding process performed by thethree-dimensional data encoding device.

First, the three-dimensional data encoding device encodes geometryinformation (geometry) (S3001). For example, the three-dimensional dataencoding is performed using octree representation.

When the positions of three-dimensional points changed by quantization,etc, after the encoding of the geometry information, thethree-dimensional data encoding device re-assigns attribute informationof the original three-dimensional points to the post-changethree-dimensional points (S3002). For example, the three-dimensionaldata encoding device interpolates values of attribute informationaccording to the amounts of change in position to re-assign theattribute information. For example, the three-dimensional data encodingdevice detects pre-change N three-dimensional points closer to thepost-change three-dimensional positions, and performs weighted averagingof the values of attribute information of the N three-dimensionalpoints. For example, the three-dimensional data encoding devicedetermines weights based on distances from the post-changethree-dimensional positions to the respective N three-dimensionalpositions in weighted averaging. The three-dimensional data encodingdevice then determines the values obtained through the weightedaveraging to be the values of the attribute information of thepost-change three-dimensional points. When two or more of thethree-dimensional points are changed to the same three-dimensionalposition through quantization, etc., the three-dimensional data encodingdevice may assign the average value of the attribute information of thepre-change two or more three-dimensional points as the values of theattribute information of the post-change three-dimensional points.

Next, the three-dimensional data encoding device encodes the attributeinformation (attribute) re-assigned (S3003). For example, when encodinga plurality of kinds of attribute information, the three-dimensionaldata encoding device may encode the plurality of kinds of attributeinformation in order. For example, when encoding colors and reflectancesas attribute information, the three-dimensional data encoding device maygenerate a bitstream added with the color encoding results and thereflectance encoding results after the color encoding results. It is tobe noted that the order of the plurality of encoding results ofattribute information to be added to a bitstream is not limited to theorder, and may be any order.

Alternatively, the three-dimensional data encoding device may add, to aheader for example, information indicating the start location of encodeddata of each attribute information in a bitstream. In this way, thethree-dimensional data decoding device is capable of selectivelydecoding attribute information required to be decoded, and thus iscapable of skipping the decoding process of the attribute informationnot required to be decoded. Accordingly, it is possible to reduce theamount of processing by the three-dimensional data decoding device.Alternatively, the three-dimensional data encoding device may encode aplurality of kinds of attribute information in parallel, and mayintegrate the encoding results into a single bitstream. In this way, thethree-dimensional data encoding device is capable of encoding theplurality of kinds of attribute information at high speed;

FIG. 60 is a flowchart of an attribute information encoding process(S3003). First, the three-dimensional data encoding device sets LoDs(S3011). In other words, the three-dimensional data encoding deviceassigns each of three-dimensional points to any one of the plurality ofLoDs.

Next, the three-dimensional data encoding device starts a loop for eachLoD (S3012). In other words, the three-dimensional data encoding deviceiteratively performs the processes of Steps from S3013 to S3021 for eachLoD.

Next, the three-dimensional data encoding device starts a loop for eachthree-dimensional point (S3013). In other words, the three-dimensionaldata encoding device iteratively performs the processes of Steps fromS3014 to S3020 for each three-dimensional point.

First, the three-dimensional data encoding device searches a pluralityof neighbor points which are three-dimensional points present in theneighborhood of a current three-dimensional point to be processed andare to be used to calculate a predicted value of the currentthree-dimensional point (S3014). Next, the three-dimensional dataencoding device calculates the weighted average of the values ofattribute information of the plurality of neighbor points, and sets theresulting value to predicted value P (S3015). Next, thethree-dimensional data encoding device calculates a prediction residualwhich is the difference between the attribute information of the currentthree-dimensional point and the predicted value (S3016). Next, thethree-dimensional data encoding device quantizes the prediction residualto calculate a quantized value (S3017). Next, the three-dimensional dataencoding device arithmetic encodes the quantized value (S3018).

Next, the three-dimensional data encoding device inverse quantizes thequantized value to calculate an inverse quantized value (S3019). Next,the three-dimensional data encoding device adds a prediction value tothe inverse quantized value to generate a decoded value (S3020). Next,the three-dimensional data encoding device ends the loop for eachthree-dimensional point (S3021). Next, the three-dimensional dataencoding device ends the loop for each LoD (S3022).

Hereinafter, a description is given of a three-dimensional data decodingprocess in the three-dimensional data decoding device which decodes abitstream generated by the three-dimensional data encoding device.

The three-dimensional data decoding device generates decoded binary databy arithmetic decoding the binary data of the attribute information inthe bitstream generated by the three-dimensional data encoding device,according to the method similar to the one performed by thethree-dimensional data encoding device. It is to be noted that whenmethods of applying arithmetic encoding are switched between the part(n-bit code) binarized using n bits and the part (remaining code)binarized using exponential-Golomb coding in the three-dimensional dataencoding device, the three-dimensional data decoding device performsdecoding in conformity with the arithmetic encoding, when applyingarithmetic decoding.

For example, the three-dimensional data decoding device performsarithmetic decoding using coding tables (decoding tables) different foreach bit in the arithmetic decoding of the n-bit code. At this time, thethree-dimensional data decoding device may change the number of codingtables to be used for each bit. For example, the three-dimensional datadecoding device performs arithmetic decoding using one coding table forfirst bit b0 in the n-bit code. The three-dimensional data decodingdevice uses two coding tables for the next bit b1. The three-dimensionaldata decoding device switches coding tables to be used for arithmeticdecoding of bit b1 according to the value (0 or 1) of b0. Likewise, thethree-dimensional data decoding device uses four coding tables for thenext bit b2. The three-dimensional data decoding device switches codingtables to be used for arithmetic decoding of bit b2 according to thevalues (in the range from 0 to 3) of b0 and b1.

In this way, the three-dimensional data decoding device uses 2^(n-1)coding tables when arithmetic decoding each bit bn−1 in the n-bit code.The three-dimensional data decoding device switches coding tables to beused according to the values (occurrence patterns) of bits before bn−1.In this way, the three-dimensional data decoding device is capable ofappropriately decoding a bitstream encoded at an increased codingefficiency using the coding tables appropriate for each bit.

It is to be noted that the three-dimensional data decoding device mayreduce the number of coding tables to be used for each bit. For example,the three-dimensional data decoding device may switch 2^(m) codingtables according to the values (occurrence patterns) of m bits (m<n−1)before bn−1 when arithmetic decoding each bit bn−1. In this way, thethree-dimensional data decoding device is capable of appropriatelydecoding the bitstream encoded at the increased coding efficiency whilereducing the number of coding tables to be used for each bit. It is tobe noted that the three-dimensional data decoding device may update theoccurrence probabilities of 0 and 1 in each coding table according tothe values of binary data occurred actually. In addition, thethree-dimensional data decoding device may fix the occurrenceprobabilities of 0 and 1 in coding tables for some bit(s). In this way,it is possible to reduce the number of updates of occurrenceprobabilities, and thus to reduce the processing amount.

For example, when an n-bit code is b0, b1, b2, . . . , bn−1, the codingtable for b0 is one (CTb0). Coding tables for b1 are two tables (CTb10and CTb11). Coding tables to be used are switched according to the value(0 or 1) of b0. Coding tables for b2 are four tables (CTb20, CTb21,CTb22, and CTb23). Coding tables to be used according to the values (inthe range from 0 to 3) of b0 and b1. Coding tables for bn−1 are 2^(n-1)tables (CTbn0, CTbn1, . . . , Tbn (2^(n-1)−1)). Coding tables to be usedare switched according to the values (in the range from 0 to 2^(n-1)−1)of b0, b1, . . . , bn−2;

FIG. 61 is a diagram for illustrating processing in the case whereremaining codes are exponential-Golomb codes. As indicated in FIG. 61 ,the part (remaining part) binarized and encoded by the three-dimensionaldata encoding device using exponential-Golomb coding includes a prefixand a suffix. For example, the three-dimensional data decoding deviceswitches coding tables between the prefix and the suffix. In otherwords, the three-dimensional data decoding device arithmetic decodeseach of bits included in the prefix using coding tables for the prefix,and arithmetic decodes each of bits included in the suffix using codingtables for the suffix.

It is to be noted that the three-dimensional data decoding device mayupdate the occurrence probabilities of 0 and 1 in each coding tableaccording to the values of binary data occurred at the time of decoding.In addition, the three-dimensional data decoding device may fix theoccurrence probabilities of 0 and 1 in one of coding tables. In thisway, it is possible to reduce the number of updates of occurrenceprobabilities, and thus to reduce the processing amount. For example,the three-dimensional data decoding device may update the occurrenceprobabilities for the prefix, and may fix the occurrence probabilitiesfor the suffix.

Furthermore, the three-dimensional data decoding device decodes thequantized prediction residual (unsigned integer value) by debinarizingthe binary data of the prediction residual arithmetic decoded accordingto a method in conformity with the encoding method used by thethree-dimensional data encoding device. The three-dimensional datadecoding device first arithmetic decodes the binary data of an n-bitcode to calculate a value of the n-bit code. Next, the three-dimensionaldata decoding device compares the value of the n-bit code with thresholdvalue R_TH.

In the case where the value of the n-bit code and threshold value R_THmatch, the three-dimensional data decoding device determines that a bitencoded using exponential-Golomb coding is present next, and arithmeticdecodes the remaining code which is the binary data encoded usingexponential-Golomb coding. The three-dimensional data decoding devicethen calculates, from the decoded remaining code, a value of theremaining code using a reverse lookup table indicating the relationshipbetween the remaining code and the value. FIG. 62 is a diagramindicating an example of a reverse lookup table indicating relationshipsbetween remaining codes and the values thereof. Next, thethree-dimensional data decoding device adds the obtained value of theremaining code to R_TH, thereby obtaining a debinarized quantizedprediction residual.

In the opposite case where the value of the n-bit code and thresholdvalue R_TH do not match (the value of the n-bit code is smaller thanvalue R_TH), the three-dimensional data decoding device determines thevalue of the n-bit code to be the debinarized quantized predictionresidual as it is. In this way, the three-dimensional data decodingdevice is capable of appropriately decoding the bitstream generatedwhile switching the binarization methods according to the values of theprediction residuals by the three-dimensional data encoding device.

It is to be noted that, when threshold value R_TH is added to, forexample, a header of a bitstream, the three-dimensional data decodingdevice may decode threshold value R_TH from the header, and may switchdecoding methods using decoded threshold value R_TH. When thresholdvalue R_TH is added to, for example, a header for each LoD, thethree-dimensional data decoding device switch decoding methods usingthreshold value R_TH decoded for each LoD.

For example, when threshold value R_TH is 63 and the value of thedecoded n-bit code is 63, the three-dimensional data decoding devicedecodes the remaining code using exponential-Golomb coding, therebyobtaining the value of the remaining code. For example, in the exampleindicated in FIG. 62 , the remaining code is 00100, and 3 is obtained asthe value of the remaining code. Next, the three-dimensional datadecoding device adds 63 that is threshold value R_TH and 3 that is thevalue of the remaining code, thereby obtaining 66 that is the value ofthe prediction residual.

In addition, when the value of the decoded n-bit code is 32, thethree-dimensional data decoding device sets 32 that is the value of then-bit code to the value of the prediction residual.

In addition, the three-dimensional data decoding device converts thedecoded quantized prediction residual, for example, from an unsignedinteger value to a signed integer value, through processing inverse tothe processing in the three-dimensional data encoding device. In thisway, when entropy decoding the prediction residual, thethree-dimensional data decoding device is capable of appropriatelydecoding the bitstream generated without considering occurrence of anegative integer. It is to be noted that the three-dimensional datadecoding device does not always need to convert an unsigned integervalue to a signed integer value, and that, for example, thethree-dimensional data decoding device may decode a sign bit whendecoding a bitstream generated by separately entropy encoding the signbit.

The three-dimensional data decoding device performs decoding by inversequantizing and reconstructing the quantized prediction residual afterbeing converted to the signed integer value, to obtain a decoded value.The three-dimensional data decoding device uses the generated decodedvalue for prediction of a current three-dimensional point to be decodedand the following three-dimensional point(s). More specifically, thethree-dimensional data decoding device multiplies the quantizedprediction residual by a decoded quantization scale to calculate aninverse quantized value and adds the inverse quantized value and thepredicted value to obtain the decoded value.

The decoded unsigned integer value (unsigned quantized value) isconverted into a signed integer value through the processing indicatedbelow. When the least significant bit (LSB) of decoded unsigned integervalue a2u is 1, the three-dimensional data decoding device sets signedinteger value a2q to −((a2u+1)>>1). When the LSB of unsigned integervalue a2u is not 1, the three-dimensional data decoding device setssigned integer value a2q to ((a2u>>1).

Likewise, when an LSB of decoded unsigned integer value b2u is 1, thethree-dimensional data decoding device sets signed integer value b2q to−((b2u+1)>>1). When the LSB of decoded unsigned integer value n2u is not1, the three-dimensional data decoding device sets signed integer valueb2q to ((b2u>>1).

Details of the inverse quantization and reconstruction processing by thethree-dimensional data decoding device are similar to the inversequantization and reconstruction processing in the three-dimensional dataencoding device.

Hereinafter, a description is given of a flow of processing in thethree-dimensional data decoding device. FIG. 63 is a flowchart of athree-dimensional data decoding process performed by thethree-dimensional data decoding device. First, the three-dimensionaldata decoding device decodes geometry information (geometry) from abitstream (S3031). For example, the three-dimensional data decodingdevice performs decoding using octree representation.

Next, the three-dimensional data decoding device decodes attributeinformation (attribute) from the bitstream (S3032). For example, whendecoding a plurality of kinds of attribute information, thethree-dimensional data decoding device may decode the plurality of kindsof attribute information in order. For example, when decoding colors andreflectances as attribute information, the three-dimensional datadecoding device decodes the color encoding results and the reflectanceencoding results in order of assignment in the bitstream. For example,when the reflectance encoding results are added after the color encodingresults in a bitstream, the three-dimensional data decoding devicedecodes the color encoding results, and then decodes the reflectanceencoding results. It is to be noted that the three-dimensional datadecoding device may decode, in any order, the encoding results of theattribute information added to the bitstream.

Alternatively, the three-dimensional data encoding device may add, to aheader for example, information indicating the start location of encodeddata of each attribute information in a bitstream. In this way, thethree-dimensional data decoding device is capable of selectivelydecoding attribute information required to be decoded, and thus iscapable of skipping the decoding process of the attribute informationnot required to be decoded. Accordingly, it is possible to reduce theamount of processing by the three-dimensional data decoding device. Inaddition, the three-dimensional data decoding device may decode aplurality of kinds of attribute information in parallel, and mayintegrate the decoding results into a single three-dimensional pointcloud. In this way, the three-dimensional data decoding device iscapable of decoding the plurality of kinds of attribute information athigh speed;

FIG. 64 is a flowchart of an attribute information decoding process(S3032). First, the three-dimensional data decoding device sets LoDs(S3041). In other words, the three-dimensional data decoding deviceassigns each of three-dimensional points having the decoded geometryinformation to any one of the plurality of LoDs. For example, thisassignment method is the same as the assignment method used in thethree-dimensional data encoding device.

Next, the three-dimensional data decoding device starts a loop for eachLoD (S3042). In other words, the three-dimensional data decoding deviceiteratively performs the processes of Steps from S3043 to S3049 for eachLoD.

Next, the three-dimensional data decoding device starts a loop for eachthree-dimensional point (S3043). In other words, the three-dimensionaldata decoding device iteratively performs the processes of Steps fromS3044 to S3048 for each three-dimensional point.

First, the three-dimensional data decoding device searches a pluralityof neighbor points which are three-dimensional points present in theneighborhood of a current three-dimensional point to be processed andare to be used to calculate a predicted value of the currentthree-dimensional point to be processed (S3044). Next, thethree-dimensional data decoding device calculates the weighted averageof the values of attribute information of the plurality of neighborpoints, and sets the resulting value to predicted value P (S3045). It isto be noted that these processes are similar to the processes in thethree-dimensional data encoding device.

Next, the three-dimensional data decoding device arithmetic decodes thequantized value from the bitstream (S3046). The three-dimensional datadecoding device inverse quantizes the decoded quantized value tocalculate an inverse quantized value (S3047). Next, thethree-dimensional data decoding device adds a predicted value to theinverse quantized value to generate a decoded value (S3048). Next, thethree-dimensional data decoding device ends the loop for eachthree-dimensional point (S3049). Next, the three-dimensional dataencoding device ends the loop for each LoD (S3050).

The following describes configurations of the three-dimensional dataencoding device and three-dimensional data decoding device according tothe present embodiment. FIG. 65 is a block diagram illustrating aconfiguration of three-dimensional data encoding device 3000 accordingto the present embodiment. Three-dimensional data encoding device 3000includes geometry information encoder 3001, attribute informationre-assigner 3002, and attribute information encoder 3003.

Attribute information encoder 3003 encodes geometry information(geometry) of a plurality of three-dimensional points included in aninput point cloud. Attribute information re-assigner 3002 re-assigns thevalues of attribute information of the plurality of three-dimensionalpoints included in the input point cloud, using the encoding anddecoding results of the geometry information. Attribute informationencoder 3003 encodes the re-assigned attribute information (attribute).Furthermore, three-dimensional data encoding device 3000 generates abitstream including the encoded geometry information and the encodedattribute information;

FIG. 66 is a block diagram illustrating a configuration ofthree-dimensional data decoding device 3010 according to the presentembodiment. Three-dimensional data decoding device 3010 includesgeometry information decoder 3011 and attribute information decoder3012.

Geometry information decoder 3011 decodes the geometry information(geometry) of a plurality of three-dimensional points from a bitstream.Attribute information decoder 3012 decodes the attribute information(attribute) of the plurality of three-dimensional points from thebitstream. Furthermore, three-dimensional data decoding device 3010integrates the decoded geometry information and the decoded attributeinformation to generate an output point cloud.

As described above, the three-dimensional data encoding device accordingto the present embodiment performs the process illustrated in FIG. 67 .The three-dimensional data encoding device encodes a three-dimensionalpoint having attribute information. First, the three-dimensional dataencoding device calculates a predicted value of the attributeinformation of the three-dimensional point (S3061). Next, thethree-dimensional data encoding device calculates a prediction residualwhich is the difference between the attribute information of thethree-dimensional point and the predicted value (S3062). Next, thethree-dimensional data encoding device binarizes the prediction residualto generate binary data (S3063). Next, the three-dimensional dataencoding device arithmetic encodes the binary data (S3064).

In this way, the three-dimensional data encoding device is capable ofreducing the code amount of the to-be-coded data of the attributeinformation by calculating the prediction residual of the attributeinformation, and binarizing and arithmetic encoding the predictionresidual.

For example, in arithmetic encoding (S3064), the three-dimensional dataencoding device uses coding tables different for each of bits of binarydata. By doing so, the three-dimensional data encoding device canincrease the coding efficiency.

For example, in arithmetic encoding (S3064), the number of coding tablesto be used is larger for a lower-order bit of the binary data.

For example, in arithmetic encoding (S3064), the three-dimensional dataencoding device selects coding tables to be used to arithmetic encode acurrent bit included in binary data, according to the value of ahigher-order bit with respect to the current bit. By doing so, since thethree-dimensional data encoding device can select coding tables to beused according to the value of the higher-order bit, thethree-dimensional data encoding device can increase the codingefficiency.

For example, in binarization (S3063), the three-dimensional dataencoding device: binarizes a prediction residual using a fixed bit countto generate binary data when the prediction residual is smaller than athreshold value (R_TH); and generates binary data including a first code(n-bit code) and a second code (remaining code) when the predictionresidual is larger than or equal to the threshold value (R_TH). Thefirst code is of a fixed bit count indicating the threshold value(R_TH), and the second code (remaining code) is obtained by binarizing,using exponential-Golomb coding, the value obtained by subtracting thethreshold value (R_TH) from the prediction residual. In arithmeticencoding (S3064), the three-dimensional data encoding device usesarithmetic encoding methods different between the first code and thesecond code.

With this, for example, since it is possible to arithmetic encode thefirst code and the second code using arithmetic encoding methodsrespectively suitable for the first code and the second code, it ispossible to increase coding efficiency.

For example, the three-dimensional data encoding device quantizes theprediction residual, and, in binarization (S3063), binarizes thequantized prediction residual. The threshold value (R_TH) is changedaccording to a quantization scale in quantization. With this, since thethree-dimensional data encoding device can use the threshold valuesuitably according to the quantization scale, it is possible to increasethe coding efficiency.

For example, the second code includes a prefix and a suffix. Inarithmetic encoding (S3064), the three-dimensional data encoding deviceuses different coding tables between the prefix and the suffix. In thisway, the three-dimensional data encoding device can increase the codingefficiency.

For example, the three-dimensional data encoding device includes aprocessor and memory, and the processor performs the above process usingthe memory.

The three-dimensional data decoding device according to the presentembodiment performs the process illustrated in FIG. 68 . Thethree-dimensional data decoding device decodes a three-dimensional pointhaving attribute information. First, the three-dimensional data decodingdevice calculates a predicted value of the attribute information of athree-dimensional point (S3071). Next, the three-dimensional datadecoding device arithmetic decodes encoded data included in a bitstreamto generate binary data (S3072). Next, the three-dimensional datadecoding device debinarizes the binary data to generate a predictionresidual (S3073). Next, the three-dimensional data decoding devicecalculates a decoded value of the attribute information of thethree-dimensional point by adding the predicted value and the predictionresidual (S3074).

In this way, the three-dimensional data decoding device is capable ofappropriately decoding the bitstream of the attribute informationgenerated by calculating the prediction residual of the attributeinformation and binarizing and arithmetic decoding the predictionresidual.

For example, in arithmetic decoding (S3072), the three-dimensional datadecoding device uses coding tables different for each of bits of binarydata. With this, the three-dimensional data decoding device is capableof appropriately decoding the bitstream encoded at an increased codingefficiency.

For example, in arithmetic decoding (S3072), the number of coding tablesto be used is larger for a lower bit of the binary data.

For example, in arithmetic decoding (S3072), the three-dimensional datadecoding device selects coding tables to be used to arithmetic decode acurrent bit included in binary data, according to the value of ahigher-order bit with respect to the current bit. With this, thethree-dimensional data decoding device is capable of appropriatelydecoding the bitstream encoded at an increased coding efficiency.

For example, in debinarizaion (S3073), the three-dimensional datadecoding device debinarizes the first code (n-bit code) of a fixed bitcount included in the binary data to generate a first value. Thethree-dimensional data decoding device: determines the first value to bethe prediction residual when the first value is smaller than thethreshold value (R_TH); and, when the first value is larger than orequal to the threshold value (R_YH), generates a second value bydebinarizing the second code (remaining code) which is anexponential-Golomb code included in the binary data and adds the firstvalue and the second value, thereby generating a prediction residual. Inthe arithmetic decoding (S3072), the three-dimensional data decodingdevice uses arithmetic decoding methods different between the first codeand the second code.

With this, the three-dimensional data decoding device is capable ofappropriately decoding the bitstream encoded at an increased codingefficiency.

For example, the three dimensional data decoding device inversequantizes the prediction residual, and, in addition (S3074), adds thepredicted value and the inverse quantized prediction residual. Thethreshold value (R_TH) is changed according to a quantization scale ininverse quantization. With this, the three-dimensional data decodingdevice is capable of appropriately decoding the bitstream encoded at anincreased coding efficiency.

For example, the second code includes a prefix and a suffix. Inarithmetic decoding (S3072), the three-dimensional data decoding deviceuses different coding tables between the prefix and the suffix. Withthis, the three-dimensional data decoding device is capable ofappropriately decoding the bitstream encoded at an increased codingefficiency.

For example, the three-dimensional data decoding device includes aprocessor and memory, and the processor performs the above-describedprocess using the memory.

Embodiment 9

Predicted values may be generated by a method different from that inEmbodiment 8. Hereinafter, a three-dimensional point to be encoded isreferred to as a first three-dimensional point, and one or morethree-dimensional points in the vicinity of the first three-dimensionalpoint is referred to as one or more second three-dimensional points insome cases.

For example, in generating of a predicted value of an attributeinformation item (attribute information) of a three-dimensional point,an attribute value as it is of a closest three-dimensional point amongencoded and decoded three-dimensional points in the vicinity of athree-dimensional point to be encoded may be generated as a predictedvalue. In the generating of the predicted value, prediction modeinformation (PredMode) may be appended for each three-dimensional point,and one predicted value may be selected from a plurality of predictedvalues to allow generation of a predicted value. Specifically, forexample, it is conceivable that, for total number M of prediction modes,an average value is assigned to prediction mode 0, an attribute value ofthree-dimensional point A is assigned to prediction mode 1, . . . , andan attribute value of three-dimensional point Z is assigned toprediction mode M−1, and the prediction mode used for prediction isappended to a bitstream for each three-dimensional point. As such, afirst prediction mode value indicating a first prediction mode forcalculating, as a predicted value, an average of attribute informationitems of the surrounding three-dimensional points may be smaller than asecond prediction mode value indicating a second prediction mode forcalculating, as a predicted value, an attribute information item as itis of a surrounding three-dimensional point. Here, the “average value”as the predicted value calculated in prediction mode 0 is an averagevalue of the attribute values of the three-dimensional points in thevicinity of the three-dimensional point to be encoded;

FIG. 69 is a diagram showing a first example of a table representingpredicted values calculated in the prediction modes according to thepresent embodiment. FIG. 70 is a diagram showing examples of attributeinformation items used as the predicted values according to the presentembodiment. FIG. 71 is a diagram showing a second example of a tablerepresenting predicted values calculated in the prediction modesaccording to the present embodiment.

Number M of prediction modes may be appended to a bitstream. Number M ofprediction modes may be defined by a profile or a level of standardsrather than appended to the bitstream. Number M of prediction modes maybe also calculated from number N of three-dimensional points used forprediction. For example, number M of prediction modes may be calculatedby M=N+1.

The table in FIG. 69 is an example of a case with number N ofthree-dimensional points used for prediction being 4 and number M ofprediction modes being 5. A predicted value of an attribute informationitem of point b2 can be generated by using attribute information itemsof points a0, a1, a2, b1. In selecting one prediction mode from aplurality of prediction modes, a prediction mode for generating, as apredicted value, an attribute value of each of points a0, a1, a2, b1 maybe selected in accordance with distance information from point b2 toeach of points a0, a1, a2, b1. The prediction mode is appended for eachthree-dimensional point to be encoded. The predicted value is calculatedin accordance with a value corresponding to the appended predictionmode.

The table in FIG. 71 is, as in FIG. 69 , an example of a case withnumber N of three-dimensional points used for prediction being 4 andnumber M of prediction modes being 5. A predicted value of an attributeinformation item of point a2 can be generated by using attributeinformation items of points a0, a1. In selecting one prediction modefrom a plurality of prediction modes, a prediction mode for generating,as a predicted value, an attribute value of each of points a0 and a1 maybe selected in accordance with distance information from point a2 toeach of points a0, a1. The prediction mode is appended for eachthree-dimensional point to be encoded. The predicted value is calculatedin accordance with a value corresponding to the appended predictionmode.

When the number of neighboring points, that is, number N of surroundingthree-dimensional points is smaller than four such as at point a2 above,a prediction mode to which a predicted value is not assigned may bewritten as “not available” in the table.

Assignment of values of the prediction modes may be determined inaccordance with the distance from the three-dimensional point to beencoded. For example, prediction mode values indicating a plurality ofprediction modes decrease with decreasing distance from thethree-dimensional point to be encoded to the surroundingthree-dimensional points having the attribute information items used asthe predicted values. The example in FIG. 69 shows that points b1 a2,a1, a0 are sequentially located closer to point b2 as thethree-dimensional point to be encoded. For example, in the calculatingof the predicted value, the attribute information item of point b1 iscalculated as the predicted value in a prediction mode indicated by aprediction mode value of “1” among two or more prediction modes, and theattribute information item of point a2 is calculated as the predictedvalue in a prediction mode indicated by a prediction mode value of “2”.As such, the prediction mode value indicating the prediction mode forcalculating, as the predicted value, the attribute information item ofpoint b1 is smaller than the prediction mode value indicating theprediction mode for calculating, as the predicted value, the attributeinformation item of point a2 farther from point b2 than point b1.

Thus, a small prediction mode value can be assigned to a point that ismore likely to be predicted and selected due to a short distance,thereby reducing a bit number for encoding the prediction mode value.Also, a small prediction mode value may be preferentially assigned to athree-dimensional point belonging to the same LoD as thethree-dimensional point to be encoded;

FIG. 72 is a diagram showing a third example of a table representingpredicted values calculated in the prediction modes according to thepresent embodiment. Specifically, the third example is an example of acase where an attribute information item used as a predicted value is avalue of color information (YUV) of a surrounding three-dimensionalpoint. As such, the attribute information item used as the predictedvalue may be color information indicating a color of thethree-dimensional point.

As shown in FIG. 72 , a predicted value calculated in a prediction modeindicated by a prediction mode value of “0” is an average of Y, U, and Vcomponents defining a YUV color space. Specifically, the predicted valueincludes a weighted average Yave of Y component values Yb1, Ya2, Ya1,Ya0 corresponding to points b1, a2, a1, a0, respectively, a weightedaverage Uave of U component values Ub1, Ua2, Ua1, Ua0 corresponding topoints b1, a2, a1, a0, respectively, and a weighted average Vave of Vcomponent values Vb1, Va2, Va1, Va0 corresponding to points b1, a2, a1,a0, respectively. Predicted values calculated in prediction modesindicated by prediction mode values of “1” to “4” include colorinformation of the surrounding three-dimensional points b1, a2, a1, a0.The color information is indicated by combinations of the Y, U, and Vcomponent values.

In FIG. 72 , the color information is indicated by a value defined bythe YUV color space, but not limited to the YUV color space. The colorinformation may be indicated by a value defined by an RGB color space ora value defined by any other color space.

As such, in the calculating of the predicted value, two or more averagesor two or more attribute information items may be calculated as thepredicted values of the prediction modes. The two or more averages orthe two or more attribute information items may indicate two or morecomponent values each defining a color space.

For example, when a prediction mode indicated by a prediction mode valueof “2” in the table in FIG. 72 is selected, a Y component, a Ucomponent, and a V component as attribute values of thethree-dimensional point to be encoded may be encoded as predicted valuesYa2, Ua2, Va2. In this case, the prediction mode value of “2” isappended to the bitstream;

FIG. 73 is a diagram showing a fourth example of a table representingpredicted values calculated in the prediction modes according to thepresent embodiment. Specifically, the fourth example is an example of acase where an attribute information item used as a predicted value is avalue of reflectance information of a surrounding three-dimensionalpoint. The reflectance information is, for example, informationindicating reflectance R.

As shown in FIG. 73 , a predicted value calculated in a prediction modeindicated by a prediction mode value of “0” is weighted average Rave ofreflectances Rb1, Ra2, Ra1, Ra0 corresponding to points b1, a2, a1, a0,respectively. Predicted values calculated in prediction modes indicatedby prediction mode values of “1” to “4” are reflectances Rb1, Ra2, Ra1,Ra0 of surrounding three-dimensional points b1, a2, a1, a0,respectively.

For example, when a prediction mode indicated by a prediction mode valueof “3” in the table in FIG. 73 is selected, a reflectance as anattribute value of a three-dimensional point to be encoded may beencoded as predicted value Ra1. In this case, the prediction mode valueof “3” is appended to the bitstream.

As shown in FIGS. 72 and 73 , the attribute information item may includea first attribute information item and a second attribute informationitem different from the first attribute information item. The firstattribute information item is, for example, color information. Thesecond attribute information item is, for example, reflectanceinformation. In the calculating of the predicted value, a firstpredicted value may be calculated by using the first attributeinformation item, and a second predicted value may be calculated byusing the second attribute information item.

Embodiment 10

Hereinafter, a method using a Region Adaptive Hierarchical Transform(RAHT) will be described as another method of encoding the attributeinformation of a three-dimensional point. FIG. 74 is a diagram fordescribing the encoding of the attribute information by using a RAHT.

First, the three-dimensional data encoding device generates Morton codesbased on the geometry information of three-dimensional points, and sortsthe attribute information of the three-dimensional points in the orderof the Morton codes. For example, the three-dimensional data encodingdevice may perform sorting in the ascending order of the Morton codes.Note that the sorting order is not limited to the order of the Mortoncodes, and other orders may be used.

Next, the three-dimensional data encoding device generates ahigh-frequency component and a low-frequency component of the layer L byapplying the Haar conversion to the attribute information of twoadjacent three-dimensional points in the order of the Morton codes. Forexample, the three-dimensional data encoding device may use the Haarconversion of 2×2 matrices. The generated high-frequency component isincluded in a coding coefficient as the high-frequency component of thelayer L, and the generated low-frequency component is used as the inputvalue for the higher layer L+1 of the layer L.

After generating the high-frequency component of the layer L by usingthe attribute information of the layer L, the three-dimensional dataencoding device subsequently performs processing of the layer L+1. Inthe processing of the layer L+1, the three-dimensional data encodingdevice generates a high-frequency component and a low-frequencycomponent of the layer L+1 by applying the Haar conversion to twolow-frequency components obtained by the Haar conversion of theattribute information of the layer L. The generated high-frequencycomponent is included in a coding coefficient as the high-frequencycomponent of the layer L+1, and the generated low-frequency component isused as the input value for the higher layer L+2 of the layer L+1.

The three-dimensional data encoding device repeats such layerprocessing, and determines that the highest layer Lmax has been reachedat the time when a low-frequency component that is input to a layerbecomes one. The three-dimensional data encoding device includes thelow-frequency component of the layer Lmax−1 that is input to the LayerLmax in a coding coefficient. Then, the value of the low-frequencycomponent or high-frequency component included in the coding coefficientis quantized, and is encoded by using entropy encoding or the like.

Note that, when only one three-dimensional point exists as two adjacentthree-dimensional points at the time of application of the Haarconversion, the three-dimensional data encoding device may use the valueof the attribute information of the existing one three-dimensional pointas the input value for a higher layer.

In this manner, the three-dimensional data encoding devicehierarchically applies the Haar conversion to the input attributeinformation, generates a high-frequency component and a low-frequencycomponent of the attribute information, and performs encoding byapplying quantization described later or the like. Accordingly, thecoding efficiency can be improved.

When the attribute information is N dimensional, the three-dimensionaldata encoding device may independently apply the Haar conversion foreach dimension, and may calculate each coding coefficient. For example,when the attribute information is color information (RGB, YUV, or thelike), the three-dimensional data encoding device applies the Haarconversion for each component, and calculates each coding coefficient.

The three-dimensional data encoding device may apply the Haar conversionin the order of the layers L, L+1, . . . , Lmax. The closer to the layerLmax, a coding coefficient including the more low-frequency componentsof the input attribute information is generated.

w0 and w1 shown in FIG. 74 are the weights assigned to eachthree-dimensional point. For example, the three-dimensional dataencoding device may calculate the weight based on the distanceinformation between two adjacent three-dimensional points to which theHaar conversion is applied, or the like. For example, thethree-dimensional data encoding device may improve the coding efficiencysuch that the closer the distance, the greater the weight. Note that thethree-dimensional data encoding device may calculate this weight withanother technique, or need not use the weight.

In the example shown in FIG. 74 , the pieces of the input attributeinformation are a0, a1, a2, a3, a4, and a5. Additionally, Ta1, Ta5, Tb1,Tb3, Tc1, and d0 are encoded among the coding coefficients after theHaar conversion. The other coding coefficients (b0, b2, c0 and the like)are medians, and are not encoded.

Specifically, in the example shown in FIG. 74 , the high-frequencycomponent Ta1 and the low-frequency component b0 are generated byperforming the Haar conversion on a0 and a1. Here, when the weights w0and w1 are equal, the low-frequency component b0 is the average value ofa0 and a1, and the high-frequency component Ta1 is the differencebetween a0 and a1.

Since there is no attribute information to be paired with a2, a2 is usedas b1 as is. Similarly, since there is no attribute information to bepaired with a3, a3 is used as b2 as is. Additionally, the high-frequencycomponent Ta5 and the low-frequency component b3 are generated byperforming the Haar conversion on a4 and a5.

In the layer L+1, the high-frequency component Tb1 and the low-frequencycomponent c0 are generated by performing the Haar conversion on b0 andb1. Similarly, the high-frequency component Tb3 and the low-frequencycomponent c1 are generated by performing the Haar conversion on b2 andb3.

In the layer Lmax−1, the High-frequency component Tc1 and thelow-frequency component d0 are generated by performing the Haarconversion on c0 and c1.

The three-dimensional data encoding device may encode the codingcoefficients to which the Haar conversion has been applied, afterquantizing the coding coefficients. For example, the three-dimensionaldata encoding device performs quantization by dividing the codingcoefficient by the quantization scale (also called the quantization step(QS)). In this case, the smaller the quantization scale, the smaller theerror (quantization error) that may occur due to quantization.Conversely, the larger the quantization scale, the larger thequantization error.

Note that the three-dimensional data encoding device may change thevalue of the quantization scale for each layer. FIG. 75 is a diagramshowing an example of setting the quantization scale for each layer. Forexample, the three-dimensional data encoding device sets smallerquantization scales to the higher layers, and larger quantization scalesto the lower layers. Since the coding coefficients of thethree-dimensional points belonging to the higher layers include morelow-frequency components than the lower layers, there is a highpossibility that the coding coefficients are important components inhuman visual characteristics and the like. Therefore, by suppressing thequantization error that may occur in the higher layers by making thequantization scales for the higher layers small, visual deteriorationcan be suppressed, and the coding efficiency can be improved.

Note that the three-dimensional data encoding device may add thequantization scale for each layer to a header or the like. Accordingly,the three-dimensional decoding device can correctly decode thequantization scale, and can appropriately decode a bitstream.

Additionally, the three-dimensional data encoding device may adaptivelyswitch the value of the quantization scale according to the importanceof a current three-dimensional point to be encoded. For example, thethree-dimensional data encoding device uses a small quantization scalefor a three-dimensional point with high importance, and uses a largequantization scale for a three-dimensional point with low importance.For example, the three-dimensional data encoding device may calculatethe importance from the weight at the time of the Haar conversion, orthe like. For example, the three-dimensional data encoding device maycalculate the quantization scale by using the sum of w0 and w1. In thismanner, by making the quantization scale of a three-dimensional pointwith high importance small, the quantization error becomes small, andthe coding efficiency can be improved.

Additionally, the value of the QS may be made smaller for the higherlayers. Accordingly, the higher the layer, the larger the value of theQW, and the prediction efficiency can be improved by suppressing thequantization error of the three-dimensional point.

Here, a coding coefficient Ta1q after quantization of the codingcoefficient Ta1 of the attribute information a1 is represented byTa1/QS_L. Note that QS may be the same value in all the layers or a partof the layers.

The QW (Quantization Weight) is the value that represents the importanceof a current three-dimensional point to be encoded. For example, theabove-described sum of w0 and w1 may be used as the QW. Accordingly, thehigher the layer, the larger the value of the QW, and the predictionefficiency can be improved by suppressing the quantization error of thethree-dimensional point.

For example, the three-dimensional data encoding device may firstinitialize the values of the QWs of all the three-dimensional pointswith 1, and may update the QW of each three-dimensional point by usingthe values of w0 and w1 at the time of the Haar conversion.Alternatively, the three-dimensional data encoding device may change theinitial value according to the layers, without initializing the valuesof the QWs of all the three-dimensional points with a value of 1. Forexample, the quantization scales for the higher layers becomes small bysetting larger QW initial values for the higher layers. Accordingly,since the prediction error in the higher layers can be suppressed, theprediction accuracy of the lower layers can be increased, and the codingefficiency can be improved. Note that the three-dimensional dataencoding device need not necessarily use the QW.

When using the QW, the quantized value Ta1q of Ta1 is calculated by(Equation K1) and (Equation K2).

$\begin{matrix}{{{Ta}1q} = {\frac{{Ta1} + \frac{{QS}_{-}L}{2}}{{QS}_{-}{LoD}1} \times {QWTa}1}} & ( {{Equation}{K1}} )\end{matrix}$ $\begin{matrix}{{{QWTa}1} = {1 + {\sum\limits_{i = 0}^{1}w_{i}}}} & ( {{Equation}{K2}} )\end{matrix}$

Additionally, the three-dimensional data encoding device scans andencodes the coding coefficients (unsigned integer values) afterquantization in a certain order. For example, the three-dimensional dataencoding device encodes a plurality of three-dimensional points from thethree-dimensional points included in the higher layers toward the lowerlayers in order.

For example, in the example shown in FIG. 74 , the three-dimensionaldata encoding device encodes a plurality of three-dimensional points inthe order of Tc1q Tb1q, Tb3q, Ta1q, and Ta5q from d0q included in thehigher layer Lmax. Here, there is a tendency that the lower the layer L,the more likely it is that the coding coefficient after quantizationbecomes 0. This can be due to the following and the like.

Since the coding coefficient of the lower layer L shows a higherfrequency component than the higher layers, there is a tendency that thecoding coefficient becomes 0 depending on a current three-dimensionalpoint. Additionally, by switching the quantization scale according tothe above-described importance or the like, the lower the layer, thelarger the quantization scales, and the more likely it is that thecoding coefficient after quantization becomes 0.

In this manner, the lower the layer, the more likely it is that thecoding coefficient after quantization becomes 0, and the value 0consecutively occurs in the first code sequence. FIG. 76 is a diagramshowing an example of the first code sequence and the second codesequence.

The three-dimensional data encoding device counts the number of timesthat the value 0 occurs in the first code sequence, and encodes thenumber of times that the value 0 consecutively occurs, instead of theconsecutive values 0. That is, the three-dimensional data encodingdevice generates a second code sequence by replacing the codingcoefficient of the consecutive values 0 in the first code sequence withthe number of consecutive times (ZeroCnt) of 0. Accordingly, when thereare consecutive values 0 of the coding coefficients after quantization,the coding efficiency can be improved by encoding the number ofconsecutive times of 0, rather than encoding a lot of 0s.

Additionally, the three-dimensional data encoding device may entropyencode the value of ZeroCnt. For example, the three-dimensional dataencoding device binarizes the value of ZeroCnt with the truncated unarycode of the total number T of the encoded three-dimensional points, andarithmetically encodes each bit after the binarization. FIG. 77 is adiagram showing an example of the truncated unary code in the case wherethe total number of encoded three-dimensional points is T. At this time,the three-dimensional data encoding device may improve the codingefficiency by using a different coding table for each bit. For example,the three-dimensional data encoding device uses coding table 1 for thefirst bit, uses coding table 2 for the second bit, and coding table 3for the subsequent bits. In this manner, the three-dimensional dataencoding device can improve the coding efficiency by switching thecoding table for each bit.

Additionally, the three-dimensional data encoding device mayarithmetically encode ZeroCnt after binarizing ZeroCnt with anExponential-Golomb. Accordingly, when the value of ZeroCnt easilybecomes large, the efficiency can be more improved than the binarizedarithmetic encoding with the truncated unary code. Note that thethree-dimensional data encoding device may add a flag for switchingbetween using the truncated unary code and using the Exponential-Golombto a header. Accordingly, the three-dimensional data encoding device canimprove the coding efficiency by selecting the optimum binarizationmethod. Additionally, the three-dimensional data decoding device cancorrectly decode a bitstream by referring to the flag included in theheader to switch the binarization method.

The three-dimensional decoding device may convert the decoded codingcoefficient after the quantization from an unsigned integer value to asigned integer value with a method contrary to the method performed bythe three-dimensional data encoding device. Accordingly, when the codingcoefficient is entropy encoded, the three-dimensional decoding devicecan appropriately decode a bitstream generated without considering theoccurrence of a negative integer. Note that the three-dimensionaldecoding device does not necessarily need to convert the codingcoefficient from an unsigned integer value to a signed integer value.For example, when decoding a bitstream including an encoded bit that hasbeen separately entropy encoded, the three-dimensional decoding devicemay decode the sign bit.

The three-dimensional decoding device decodes the coding coefficientafter the quantization converted to the signed integer value, by theinverse quantization and the inverse Haar conversion. Additionally, thethree-dimensional decoding device utilizes the coding coefficient afterthe decoding for the prediction after the current three-dimensionalpoint to be decoded. Specifically, the three-dimensional decoding devicecalculates the inverse quantized value by multiplying the codingcoefficient after the quantization by the decoded quantization scale.Next, the three-dimensional decoding device obtains the decoded value byapplying the inverse Haar conversion described later to the inversequantized value.

For example, the three-dimensional decoding device converts the decodedunsigned integer value to a signed integer value with the followingmethod. When the LSB (least significant bit) of the decoded unsignedinteger value a2u is 1, the signed integer value Ta1q is set to−((a2u+1)>>1). When the LSB of the decoded unsigned integer value a2u isnot 1 (when it is 0), the signed integer value Ta1q is set to (a2u>>1).

Additionally, the inverse quantized value of Ta1 is represented byTa1q×QS_L. Here, Ta1q is the quantized value of Ta1. In addition, QS_Lis the quantization step for the layer L.

Additionally, the QS may be the same value for all the layers or a partof the layers. In addition, the three-dimensional data encoding devicemay add the information indicating the QS to a header or the like.Accordingly, the three-dimensional decoding device can correctly performinverse quantization by using the same QS as the QS used by thethree-dimensional data encoding device.

Next, the inverse Haar conversion will be described. FIG. 78 is adiagram for describing the inverse Haar conversion. Thethree-dimensional decoding device decodes the attribute value of athree-dimensional point by applying the inverse Haar conversion to thecoding coefficient after the inverse quantization.

First, the three-dimensional decoding device generates the Morton codesbased on the geometry information of three-dimensional points, and sortsthe three-dimensional points in the order of the Morton codes. Forexample, the three-dimensional decoding device may perform the sortingin ascending order of the Morton codes. Note that the sorting order isnot limited to the order of the Morton codes, and the other order may beused.

Next, the three-dimensional decoding device restores the attributeinformation of three-dimensional points that are adjacent to each otherin the order of the Morton codes in the layer L, by applying the inverseHaar conversion to the coding coefficient including the low-frequencycomponent of the layer L+1, and the coding coefficient including thehigh-frequency component of the layer L. For example, thethree-dimensional decoding device may use the inverse Haar conversion ofa 2×2 matrix. The attribute information of the restored layer L is usedas the input value for the lower layer L−1.

The three-dimensional decoding device repeats such layer processing, andends the processing when all the attribute information of the bottomlayer is decoded. Note that, when only one three-dimensional pointexists as two three-dimensional points that are adjacent to each otherin the layer L−1 at the time of application of the inverse Haarconversion, the three-dimensional decoding device may assign the valueof the encoding component of the layer L to the attribute value of theone existing three-dimensional point. Accordingly, the three-dimensionaldecoding device can correctly decode a bitstream with improved codingefficiency by applying the Haar conversion to all the values of theinput attribute information.

When the attribute information is N dimensional, the three-dimensionaldecoding device may independently apply the inverse Haar conversion foreach dimension, and may decode each coding coefficient. For example,when the attribute information is color information (RGB, YUV, or thelike), the three-dimensional data decoding device applies the inverseHaar conversion to the coding coefficient for each component, anddecodes each attribute value.

The three-dimensional decoding device may apply the inverse Haarconversion in the order of Layers Lmax, L+1, . . . , L. Additionally, w0and w1 shown in FIG. 78 are the weights assigned to eachthree-dimensional point. For example, the three-dimensional datadecoding device may calculate the weight based on the distanceinformation between two adjacent three-dimensional points to which theinverse Haar conversion is applied, or the like. For example, thethree-dimensional data encoding device may decode a bitstream withimproved coding efficiency such that the closer the distance, thegreater the weight.

In the example shown in FIG. 78 , the coding coefficients after theinverse quantization are Ta1, Ta5, Tb1, Tb3, Tc1, and d0, and a0, a1,a2, a3, a4, and a5 are obtained as the decoded values.

FIG. 79 is a diagram showing a syntax example of the attributeinformation (attribute data). The attribute information (attribute data)includes the number of consecutive zeros (ZeroCnt), the number ofattribute dimensions (attribute_dimension), and the coding coefficient(value [j] [i]).

The number of consecutive zeros (ZeroCnt) indicates the number of timesthat the value 0 continues in the coding coefficient after quantization.Note that the three-dimensional data encoding device may arithmeticallyencode ZeroCnt after binarizing ZeroCnt.

Additionally, as shown in FIG. 79 , the three-dimensional data encodingdevice may determine whether or not the layer L (layerL) to which thecoding coefficient belongs is equal to or more than a predefinedthreshold value TH_layer, and may switch the information added to abitstream according to the determination result. For example, when thedetermination result is true, the three-dimensional data encoding deviceadds all the coding coefficients of the attribute information to abitstream. In addition, when the determination result is false, thethree-dimensional data encoding device may add a part of the codingcoefficients to a bitstream.

Specifically, when the determination result is true, thethree-dimensional data encoding device adds the encoded result of thethree-dimensional information of the color information RGB or YUV to abitstream. When the determination result is false, the three-dimensionaldata encoding device may add a part of information such as G or Y of thecolor information to a bitstream, and need not to add the othercomponents to the bitstream. In this manner, the three-dimensional dataencoding device can improve the coding efficiency by not adding a partof the coding coefficients of the layer (the layer smaller thanTH_layer) including the coding coefficients indicating thehigh-frequency component with less visually noticeable degradation to abitstream.

The number of attribute dimensions (attribute_dimension) indicates thenumber of dimensions of the attribute information. For example, when theattribute information is the color information (RGB, YUV, or the like)of a three-dimensional point, since the color information isthree-dimensional, the number of attribute dimensions is set to a value3. When the attribute information is the reflectance, since thereflectance is one-dimensional, the number of attribute dimensions isset to a value 1. Note that the number of attribute dimensions may beadded to the header of the attribute information of a bit stream or thelike.

The coding coefficient (value [j] [i]) indicates the coding coefficientafter quantization of the attribute information of the j-th dimension ofthe i-th three-dimensional point. For example, when the attributeinformation is color information, value [99] [1] indicates the codingcoefficient of the second dimension (for example, the G value) of the100th three-dimensional point. Additionally, when the attributeinformation is reflectance information, value [119] [0] indicates thecoding coefficient of the first dimension (for example, the reflectance)of the 120th three-dimensional point.

Note that, when the following conditions are satisfied, thethree-dimensional data encoding device may subtract the value 1 fromvalue [j] [i], and may entropy encode the obtained value. In this case,the three-dimensional data decoding device restores the codingcoefficient by adding the value 1 to value [j] [i] after entropydecoding.

The above-described conditions are (1) when attribute_dimension=1, or(2) when attribute_dimension is 1 or more, and when the values of allthe dimensions are equal. For example, when the attribute information isthe reflectance, since attribute_dimension=1, the three-dimensional dataencoding device subtracts the value 1 from the coding coefficient tocalculate value, and encodes the calculated value. The three-dimensionaldecoding device calculates the coding coefficient by adding the value 1to the value after decoding.

More specifically, for example, when the coding coefficient of thereflectance is 10, the three-dimensional data encoding device encodesthe value 9 obtained by subtracting the value 1 from the value 10 of thecoding coefficient. The three-dimensional data decoding device adds thevalue 1 to the decoded value 9 to calculate the value 10 of the codingcoefficient.

Additionally, since attribute_dimension=3 when the attribute informationis the color, for example, when the coding coefficient afterquantization of each of the components R, G, and B is the same, thethree-dimensional data encoding device subtracts the value 1 from eachcoding coefficient, and encodes the obtained value. Thethree-dimensional data decoding device adds the value 1 to the valueafter decoding. More specifically, for example, when the codingcoefficient of R, G, and B=(1, 1, 1), the three-dimensional dataencoding device encodes (0, 0, 0). The three-dimensional data decodingdevice adds 1 to each component of (0, 0, 0) to calculate (1, 1, 1).Additionally, when the coding coefficients of R, G, and B=(2, 1, 2), thethree-dimensional data encoding device encodes (2, 1, 2) as is. Thethree-dimensional data decoding device uses the decoded (2, 1, 2) as isas the coding coefficients.

In this manner, by providing ZeroCnt, since the pattern in which all thedimensions are 0 as value is not generated, the value obtained bysubtracting 1 from the value indicated by value can be encoded.Therefore, the coding efficiency can be improved.

Additionally, value [0] [i] shown in FIG. 79 indicates the codingcoefficient after quantization of the attribute information of the firstdimension of the i-th three-dimensional point. As shown in FIG. 79 ,when the layer L (layerL) to which the coding coefficient belongs issmaller than the threshold value TH_layer, the code amount may bereduced by adding the attribute information of the first dimension to abitstream (not adding the attribute information of the second andfollowing dimensions to the bitstream).

The three-dimensional data encoding device may switch the calculationmethod of the value of ZeroCnt depending on the value ofattribute_dimension. For example, when attribute_dimension=3, thethree-dimensional data encoding device may count the number of timesthat the values of the coding coefficients of all the components(dimensions) become 0. FIG. 80 is a diagram showing an example of thecoding coefficient and ZeroCnt in this case. For example, in the case ofthe color information shown in FIG. 80 , the three-dimensional dataencoding device counts the number of the consecutive coding coefficientshaving 0 for all of the R, G, and B components, and adds the countednumber to a bitstream as ZeroCnt. Accordingly, it becomes unnecessary toencode ZeroCnt for each component, and the overhead can be reduced.Therefore, the coding efficiency can be improved. Note that thethree-dimensional data encoding device may calculate ZeroCnt for eachdimension even when attribute_dimension is two or more, and may add thecalculated ZeroCnt to a bitstream.

FIG. 81 is a flowchart of the three-dimensional data encoding processingaccording to the present embodiment. First, the three-dimensional dataencoding device encodes geometry information (geometry) (S6601). Forexample, the three-dimensional data encoding device performs encoding byusing an octree representation.

Next, the three-dimensional data encoding device converts the attributeinformation (S6602). For example, after the encoding of the geometryinformation, when the position of a three-dimensional point is changeddue to quantization or the like, the three-dimensional data encodingdevice reassigns the attribute information of the originalthree-dimensional point to the three-dimensional point after the change.Note that the three-dimensional data encoding device may interpolate thevalue of the attribute information according to the amount of change ofthe position to perform the reassignment. For example, thethree-dimensional data encoding device detects N three-dimensionalpoints before the change near the three dimensional position after thechange, performs the weighted averaging of the value of the attributeinformation of the N three-dimensional points based on the distance fromthe three-dimensional position after the change to each of the Nthree-dimensional points, and sets the obtained value as the value ofthe attribute information of the three-dimensional point after thechange. Additionally, when two or more three-dimensional points arechanged to the same three-dimensional position due to quantization orthe like, the three-dimensional data encoding device may assign theaverage value of the attribute information in the two or morethree-dimensional points before the change as the value of the attributeinformation after the change.

Next, the three-dimensional data encoding device encodes the attributeinformation (S6603). For example, when encoding a plurality of pieces ofattribute information, the three-dimensional data encoding device mayencode the plurality of pieces of attribute information in order. Forexample, when encoding the color and the reflectance as the attributeinformation, the three-dimensional data encoding device generates abitstream to which the encoding result of the reflectance is added afterthe encoding result of the color. Note that a plurality of encodingresults of the attribute information added to a bitstream may be in anyorder.

Additionally, the three-dimensional data encoding device may add theinformation indicating the start location of the encoded data of eachattribute information in a bitstream to a header or the like.Accordingly, since the three-dimensional data decoding device canselectively decode the attribute information that needs to be decoded,the decoding processing of the attribute information that does not needto be decoded can be omitted. Therefore, the processing amount for thethree-dimensional data decoding device can be reduced. Additionally, thethree-dimensional data encoding device may encode a plurality of piecesof attribute information in parallel, and may integrate the encodingresults into one bitstream. Accordingly, the three-dimensional dataencoding device can encode a plurality of pieces of attributeinformation at high speed.

FIG. 82 is a flowchart of the attribute information encoding processing(S6603). First, the three-dimensional data encoding device generates acoding coefficient from attribute information by the Haar conversion(S6611). Next, the three-dimensional data encoding device appliesquantization to the coding coefficient (S6612). Next, thethree-dimensional data encoding device generates encoded attributeinformation (bitstream) by encoding the coding coefficient after thequantization (S6613).

Additionally, the three-dimensional data encoding device applies inversequantization to the coding coefficient after the quantization (S6614).Next, the three-dimensional decoding device decodes the attributeinformation by applying the inverse Haar conversion to the codingcoefficient after the inverse quantization (S6615). For example, thedecoded attribute information is referred to in the following encoding.

FIG. 83 is a flowchart of the coding coefficient encoding processing(S6613). First, the three-dimensional data encoding device converts acoding coefficient from a signed integer value to an unsigned integervalue (S6621). For example, the three-dimensional data encoding deviceconverts a signed integer value to an unsigned integer value as follows.When signed integer value Ta1q is smaller than 0, the unsigned integervalue is set to −1−(2×Ta1q). When the signed integer value Ta1q is equalto or more than 0, the unsigned integer value is set to 2×Ta1q. Notethat, when the coding coefficient does not become a negative value, thethree-dimensional data encoding device may encode the coding coefficientas the unsigned integer value as is.

When not all coding coefficients have been processed (No in S6622), thethree-dimensional data encoding device determines whether the value ofthe coding coefficient to be processed is zero (S6623). When the valueof the coding coefficient to be processed is zero (Yes in S6623), thethree-dimensional data encoding device increments ZeroCnt by 1 (S6624),and returns to step S6622.

When the value of the coding coefficient to be processed is not zero (Noin S6623), the three-dimensional data encoding device encodes ZeroCnt,and resets ZeroCnt to zero (S6625). Additionally, the three-dimensionaldata encoding device arithmetically encodes the coding coefficient to beprocessed (S6626), and returns to step S6622. For example, thethree-dimensional data encoding device performs binary arithmeticencoding. In addition, the three-dimensional data encoding device maysubtract the value 1 from the coding coefficient, and may encode theobtained value.

Additionally, the processing of steps S6623 to S6626 is repeatedlyperformed for each coding coefficient. In addition, when all the codingcoefficients have been processed (Yes in S6622), the three-dimensionaldata encoding device ends the processing.

FIG. 84 is a flowchart of the three-dimensional data decoding processingaccording to the present embodiment. First, the three-dimensionaldecoding device decodes geometry information (geometry) from a bitstream(S6631). For example, the three-dimensional data decoding deviceperforms decoding by using an octree representation.

Next, the three-dimensional decoding device decodes the attributeinformation from the bitstream (S6632). For example, when decoding aplurality of pieces of attribute information, the three-dimensionaldecoding device may decode the plurality of pieces of attributeinformation in order. For example, when decoding the color and thereflectance as the attribute information, the three-dimensional datadecoding device decodes the encoding result of the color and theencoding result of the reflectance according to the order in which theyare added to the bitstream. For example, when the encoding result of thereflectance is added after the encoding result of the color in abitstream, the three-dimensional data decoding device decodes theencoding result of the color, and thereafter decodes the encoding resultof the reflectance. Note that the three-dimensional data decoding devicemay decode the encoding results of the attribute information added to abitstream in any order.

Additionally, the three-dimensional decoding device may obtain theinformation indicating the start location of the encoded data of eachattribute information in a bitstream by decoding a header or the like.Accordingly, since the three-dimensional data decoding device canselectively decode the attribute information that needs to be decoded,the decoding processing of the attribute information that does not needto be decoded can be omitted. Therefore, the processing amount of thethree-dimensional decoding device can be reduced. Additionally, thethree-dimensional data decoding device may decode a plurality of piecesof attribute information in parallel, and may integrate the decodingresults into one three-dimensional point cloud. Accordingly, thethree-dimensional data decoding device can decode a plurality of piecesof attribute information at high speed.

FIG. 85 is a flowchart of the attribute information decoding processing(S6632). First, the three-dimensional decoding device decodes a codingcoefficient from a bitstream (S6641). Next, the three-dimensionaldecoding device applies the inverse quantization to the codingcoefficient (S6642). Next, the three-dimensional decoding device decodesthe attribute information by applying the inverse Haar conversion to thecoding coefficient after the inverse quantization (S6643).

FIG. 86 is a flowchart of the coding coefficient decoding processing(S6641). First, the three-dimensional decoding device decodes ZeroCntfrom a bitstream (S6651). When not all coding coefficients have beenprocessed (No in S6652), the three-dimensional decoding devicedetermines whether ZeroCnt is larger than 0 (S6653).

When ZeroCnt is larger than zero (Yes in S6653), the three-dimensionaldecoding device sets the coding coefficient to be processed to 0(S6654). Next, the three-dimensional decoding device subtracts 1 fromZeroCnt (S6655), and returns to step S6652.

When ZeroCnt is zero (No in S6653), the three-dimensional decodingdevice decodes the coding coefficient to be processed (S6656). Forexample, the three-dimensional decoding device uses binary arithmeticdecoding. Additionally, the three-dimensional decoding device may addthe value 1 to the decoded coding coefficient.

Next, the three-dimensional decoding device decodes ZeroCnt, sets theobtained value to ZeroCnt (S6657), and returns to step S6652.

Additionally, the processing of steps S6653 to S6657 is repeatedlyperformed for each coding coefficient. In addition, when all the codingcoefficients have been processed (Yes in S6652), the three-dimensionaldata encoding device converts a plurality of decoded coding coefficientsfrom unsigned integer values to signed integer values (S6658). Forexample, the three-dimensional data decoding device may convert thedecoded coding coefficients from unsigned integer values to signedinteger values as follows. When the LSB (least significant bit) of thedecoded unsigned integer value Ta1u is 1, the signed integer value Ta1qis set to −((Ta1u+1)>>1). When the LSB of the decoded unsigned integervalue Ta1u is not 1 (when it is 0), the signed integer value Ta1q is setto (Ta1u>>1). Note that, when the coding coefficient does not become anegative value, the three-dimensional data decoding device may use thedecoded coding coefficient as is as the signed integer value.

FIG. 87 is a block diagram of attribute information encoder 6600included in the three-dimensional data encoding device. Attributeinformation encoder 6600 includes sorter 6601, Haar transformer 6602,quantizer 6603, inverse quantizer 6604, inverse Haar converter 6605,memory 6606, and arithmetic encoder 6607.

Sorter 6601 generates the Morton codes by using the geometry informationof three-dimensional points, and sorts the plurality ofthree-dimensional points in the order of the Morton codes. Haartransformer 6602 generates the coding coefficient by applying the Haarconversion to the attribute information. Quantizer 6603 quantizes thecoding coefficient of the attribute information.

Inverse quantizer 6604 inverse quantizes the coding coefficient afterthe quantization. Inverse Haar converter 6605 applies the inverse Haarconversion to the coding coefficient. Memory 6606 stores the values ofpieces of attribute information of a plurality of decodedthree-dimensional points. For example, the attribute information of thedecoded three-dimensional points stored in memory 6606 may be utilizedfor prediction and the like of an unencoded three-dimensional point.

Arithmetic encoder 6607 calculates ZeroCnt from the coding coefficientafter the quantization, and arithmetically encodes ZeroCnt.Additionally, arithmetic encoder 6607 arithmetically encodes thenon-zero coding coefficient after the quantization. Arithmetic encoder6607 may binarize the coding coefficient before the arithmetic encoding.In addition, arithmetic encoder 6607 may generate and encode variouskinds of header information.

FIG. 88 is a block diagram of attribute information decoder 6610included in the three-dimensional decoding device. Attribute informationdecoder 6610 includes arithmetic decoder 6611, inverse quantizer 6612,inverse Haar converter 6613, and memory 6614.

Arithmetic decoder 6611 arithmetically decodes ZeroCnt and the codingcoefficient included in a bitstream. Note that arithmetic decoder 6611may decode various kinds of header information.

Inverse quantizer 6612 inverse quantizes the arithmetically decodedcoding coefficient. Inverse Haar converter 6613 applies the inverse Haarconversion to the coding coefficient after the inverse quantization.Memory 6614 stores the values of pieces of attribute information of aplurality of decoded three-dimensional points. For example, theattribute information of the decoded three-dimensional points stored inmemory 6614 may be utilized for prediction of an undcodedthree-dimensional point.

Note that, in the above-described embodiment, although the example hasbeen shown in which the three-dimensional points are encoded in theorder of the lower layers to the higher layers as the encoding order, itis not necessarily limit to this. For example, a method may be used thatscans the coding coefficients after the Haar conversion in the order ofthe higher layers to the lower layers. Note that, also in this case, thethree-dimensional data encoding device may encode the number ofconsecutive times of the value 0 as ZeroCnt.

Additionally, the three-dimensional data encoding device may switchwhether or not to use the encoding method using ZeroCnt described in thepresent embodiment per WLD, SPC, or volume. In this case, thethree-dimensional data encoding device may add the informationindicating whether or not the encoding method using ZeroCnt has beenapplied to the header information. Accordingly, the three-dimensionaldecoding device can appropriately perform decoding. As an example of theswitching method, for example, the three-dimensional data encodingdevice counts the number of times of occurrence of the codingcoefficient having a value of 0 with respect to one volume. When thecount value exceeds a predefined threshold value, the three-dimensionaldata encoding device applies the method using ZeroCnt to the nextvolume, and when the count value is equal to or less than the thresholdvalue, the three-dimensional data encoding device does not apply themethod using ZeroCnt to the next volume. Accordingly, since thethree-dimensional data encoding device can appropriately switch whetheror not to apply the encoding method using ZeroCnt according to thecharacteristic of a current three-dimensional point to be encoded, thecoding efficiency can be improved.

Embodiment 11

Next, a quantization parameter will be described.

In order to divide point cloud data based on characteristics andpositions concerning the point cloud data, a slice and a tile are used.Here, a different quality may be required for each of the pieces ofdivisional point cloud data, because of hardware restrictions orrequirements for real-time processing, for example. For example, whenencoding point cloud data by dividing the point cloud data into sliceson an object basis, slice data including a plant is less important, sothat the resolution (quality) of the slice data can be decreased byquantization. On the other hand, the resolution (quality) of importantslice data can be increased by setting the quantization value at a lowervalue. A quantization parameter is used to enable such a control ofquantization value.

Here, data to be quantized, a scale used for the quantization, andquantized data, which is the result of calculation by the quantization,are expressed by Equations G1 and G2 below.quantized data=data/scale  (Equation G1)data=quantized data*scale  (Equation G2)

FIG. 89 is a diagram for describing a process performed by quantizer5323 that quantizes data and inverse quantizer 5333 thatinverse-quantizes quantized data.

Quantizer 5323 quantizes data using a scale. That is, quantizer 5323calculates quantized data, which is data quantized, by performing aprocess according to Equation G1.

Inverse quantizer 5333 inverse-quantizes quantized data using the scale.That is, inverse quantizer calculates inverse-quantized quantized databy performing a process according to Equation G2.

The scale and the quantization value (quantization parameter (QP) value)are expressed by Equation G3 below.quantization value(QP value)=log(scale)  (Equation G3)quantization value(QP value)=default value(reference value)+quantizationdelta(difference information)  (Equation G4)

These parameters are generically referred to as a quantizationparameter.

For example, as illustrated in FIG. 90 , a quantization value is a valuewith respect to a default value, and is calculated by adding aquantization delta to the default value. If the quantization value issmaller than the default value, the quantization delta is a negativevalue. If the quantization value is greater than the default value, thequantization delta is a positive value. If the quantization value isequal to the default value, the quantization delta is 0. When thequantization delta is 0, the quantization delta can be omitted.

An encoding process will be described. FIG. 91 is a block diagramillustrating a configuration of first encoder 5300 included in thethree-dimensional data encoding device according to the presentembodiment. FIG. 92 is a block diagram illustrating a configuration ofdivider 5301 according to the present embodiment. FIG. 93 is a blockdiagram illustrating a configuration of geometry information encoder5302 and attribute information encoder 5303 according to the presentembodiment.

First encoder 5300 generates encoded data (encoded stream) by encodingpoint cloud data in a first encoding method (geometry-based PCC (GPCC)).First encoder 5300 includes divider 5301, a plurality of geometryinformation encoders 5302, a plurality of attribute information encoders5303, additional information encoder 5304, and multiplexer 5305.

Divider 5301 generates a plurality of pieces of divisional data bydividing point cloud data. Specifically, divider 5301 generates aplurality of pieces of divisional data by dividing a space of pointcloud data into a plurality of subspaces. Here, a subspace is acombination of tiles or slices, or a combination of tiles and slices.More specifically, point cloud data includes geometry information,attribute information, and additional information. Divider 5301 dividesgeometry information into a plurality of pieces of divisional geometryinformation, and divides attribute information into a plurality ofpieces of divisional attribute information. Divider 5301 also generatesadditional information concerning the division.

As illustrated in FIG. 92 , divider 5301 includes tile divider 5311 andslice divider 5312. For example, tile divider 5311 divides a point cloudinto tiles. Tile divider 5311 may determine a quantization value usedfor each divisional tile as tile additional information.

Slice divider 5312 further divides a tile obtained by tile divider 5311into slices. Slice divider 5312 may determine a quantization value usedfor each divisional slice as slice additional information.

The plurality of geometry information encoders 5302 generate a pluralityof pieces of encoded geometry information by encoding a plurality ofpieces of divisional geometry information. For example, the plurality ofgeometry information encoders 5302 process a plurality of pieces ofdivisional geometry information in parallel.

As illustrated in FIG. 93 , geometry information encoder 5302 includesquantization value calculator 5321 and entropy encoder 5322.Quantization value calculator 5321 generates a quantization value(quantization parameter) of divisional geometry information to beencoded. Entropy encoder 5322 calculates quantized geometry informationby quantizing the divisional geometry information using the quantizationvalue (quantization parameter) generated by quantization valuecalculator 5321.

The plurality of attribute information encoders 5303 generate aplurality of pieces of encoded attribute information by encoding aplurality of pieces of divisional attribute information. For example,the plurality of attribute information encoders 5303 process a pluralityof pieces of divisional attribute information in parallel.

As illustrated in FIG. 93 , attribute information encoder 5303 includesquantization value calculator 5331 and entropy encoder 5332.Quantization value calculator 5321 generates a quantization value(quantization parameter) of divisional attribute information to beencoded. Entropy encoder 5332 calculates quantized attribute informationby quantizing the divisional attribute information using thequantization value (quantization parameter) generated by quantizationvalue calculator 5331.

Additional information encoder 5304 generates encoded additionalinformation by encoding additional information included in the pointcloud data and additional information concerning the data divisiongenerated in the division by divider 5301.

Multiplexer 5305 generates encoded data (encoded stream) by multiplexinga plurality of pieces of encoded geometry information, a plurality ofpieces of encoded attribute information, and encoded additionalinformation, and transmits the generated encoded data. The encodedadditional information is used for decoding.

Note that, although FIG. 91 shows an example in which there are twogeometry information encoders 5302 and two attribute informationencoders 5303, the number of geometry information encoders 5302 and thenumber of attribute information encoders 5303 may be one, or three ormore. The plurality of pieces of divisional data may be processed inparallel in the same chip, such as by a plurality of cores of a CPU,processed in parallel by cores of a plurality of chips, or processed inparallel by a plurality of cores of a plurality of chips.

Next, a decoding process will be described. FIG. 94 is a block diagramillustrating a configuration of first decoder 5340. FIG. 95 is a blockdiagram illustrating a configuration of geometry information decoder5342 and attribute information decoder 5343.

First decoder 5340 reproduces point cloud data by decoding encoded data(encoded stream) generated by encoding the point cloud data in the firstencoding method (GPCC). First decoder 5340 includes demultiplexer 5341,a plurality of geometry information decoders 5342, a plurality ofattribute information decoders 5343, additional information decoder5344, and combiner 5345.

Demultiplexer 5341 generates a plurality of pieces of encoded geometryinformation, a plurality of pieces of encoded attribute information, andencoded additional information by demultiplexing encoded data (encodedstream).

The plurality of geometry information decoders 5342 generate a pluralityof pieces of quantized geometry information by decoding a plurality ofpieces of encoded geometry information. For example, the plurality ofgeometry information decoders 5342 process a plurality of pieces ofencoded geometry information in parallel.

As illustrated in FIG. 95 , geometry information decoder 5342 includesquantization value calculator 5351 and entropy decoder 5352.Quantization value calculator 5351 generates a quantization value ofquantized geometry information. Entropy decoder 5352 calculates geometryinformation by inverse-quantizing the quantized geometry informationusing the quantization value generated by quantization value calculator5351.

The plurality of attribute information decoders 5343 generate aplurality of pieces of divisional attribute information by decoding aplurality of pieces of encoded attribute information. For example, theplurality of attribute information decoders 5343 process a plurality ofpieces of encoded attribute information in parallel.

As illustrated in FIG. 95 , attribute information decoder 5343 includesquantization value calculator 5361 and entropy decoder 5362.Quantization value calculator 5361 generates a quantization value ofquantized attribute information. Entropy decoder 5362 calculatesattribute information by inverse-quantizing the quantized attributeinformation using the quantization value generated by quantization valuecalculator 5361.

The plurality of additional information decoders 5344 generateadditional information by decoding encoded additional information.

Combiner 5345 generates geometry information by combining a plurality ofpieces of divisional geometry information using additional information.Combiner 5345 generates attribute information by combining a pluralityof pieces of divisional attribute information using additionalinformation. For example, combiner 5345 first generates point cloud dataassociated with a tile by combining decoded point cloud data associatedwith slices using slice additional information. Combiner 5345 thenreproduces the original point cloud data by combining point cloud dataassociated with tiles using tile additional information.

Note that, although FIG. 94 shows an example in which there are twogeometry information decoders 5342 and two attribute informationdecoders 5343, the number of geometry information decoders 5342 and thenumber of attribute information decoders 5343 may be one, or three ormore. The plurality of pieces of divisional data may be processed inparallel in the same chip, such as by a plurality of cores of a CPU,processed in parallel by cores of a plurality of chips, or processed inparallel by a plurality of cores of a plurality of chips.

[Method of Determining Quantization Parameter]

FIG. 96 is a flowchart illustrating an example of a process concerningdetermination of a quantization value (quantization parameter value: QPvalue) in the encoding of geometry information (geometry) or theencoding of attribute information (attribute).

A QP value is determined by considering the coding efficiency on a basisof data units of geometry information or attribute information forming aPCC frame, for example. When the data unit is a tile or slice resultingfrom division, the QP value is determined on a basis of divisional dataunits by considering the coding efficiency of the divisional data units.The QP value may be determined on a basis of data units before division.

As illustrated in FIG. 96 , the three-dimensional data encoding devicedetermines a QP value used for the encoding of geometry information(S5301). The three-dimensional data encoding device may determine the QPvalue for each of a plurality of divisional slices in a predeterminedmanner. Specifically, the three-dimensional data encoding devicedetermines the QP value based on the characteristics or quality of thedata of the geometry information. For example, the three-dimensionaldata encoding device may determine the density of point cloud data foreach data unit, that is, the number of points per unit area belonging toeach slice, and determine a value corresponding to the density of pointcloud data as the QP value. Alternatively, the three-dimensional dataencoding device may determine, as the QP value, any of the followingvalues corresponding to geometry information: the number of points ofpoint cloud data, the distribution of points of point cloud data, theimbalance of points of point cloud data, a feature quantity obtainedfrom information on points, the number of feature points, or arecognized object. The three-dimensional data encoding device may alsodetermine an object associated with geometry information of a map anddetermine the QP value based on the object based on the geometryinformation, or may determine the QP value based on information or afeature quantity obtained by projecting three-dimensional point cloudonto a two-dimensional plane. The corresponding QP value may be storedin a memory in advance in the form of a table that associates the QPvalue with the density, the number of points, the distribution ofpoints, or the imbalance of points of point cloud data. Thecorresponding QP value may also be stored in a memory in advance in theform of a table that associates the QP value with a feature quantity orthe number of feature points obtained from information on points or anobject recognized based on the information on points. The correspondingQP value may be determined based on a result of simulation of the codingefficiency or the like using various QP values in the encoding of thegeometry information concerning point cloud data.

The three-dimensional data encoding device then determines a referencevalue (default value) and difference information (quantization delta) onthe QP value for geometry information (S5302). Specifically, thethree-dimensional data encoding device determines a reference value anddifference information to be transmitted using the determined QP valuein a predetermined manner, and sets (adds) the determined referencevalue and difference information in at least one of the additionalinformation or the header of the data.

The three-dimensional data encoding device then determines a QP valueused for the encoding of attribute information (S5303). Thethree-dimensional data encoding device may determine the QP value foreach of a plurality of divisional slices in a predetermined manner.Specifically, the three-dimensional data encoding device determines theQP value based on the characteristics or quality of the data of theattribute information. For example, the three-dimensional data encodingdevice may determine the QP value on a basis of data units based on thecharacteristics of the attribute information. Color characteristicsinclude luminance, chromaticity, and chroma, a histogram thereof, andcolor continuity, for example. When the attribute information isreflectance, the QP value may be determined based on information basedon the reflectance. For example, when a face is detected as an objectfrom point cloud data, the three-dimensional data encoding device maydetermine a high-quality QP value for the point cloud data forming theobject detected as a face. In this way, the three-dimensional dataencoding device may determine the QP value for the point cloud dataforming an object depending on the type of the object.

When a three-dimensional point has a plurality of pieces of attributeinformation, the three-dimensional data encoding device may determine adifferent QP value for each piece of attribute information based on thepiece of attribute information. Alternatively, the three-dimensionaldata encoding device may determine a QP value for the plurality ofpieces of attribute information based on any one of the pieces ofattribute information, or determine a QP value for the plurality ofpieces of attribute information based on a plurality of pieces ofattribute information.

The three-dimensional data encoding device then determines a referencevalue (default value) and difference information (quantization delta) onthe QP value for attribute information (S5304). Specifically, thethree-dimensional data encoding device determines a reference value anddifference information to be transmitted using the determined QP valuein a predetermined manner, and sets (adds) the determined referencevalue and difference information in at least one of the additionalinformation or the header of the data.

The three-dimensional data encoding device then quantizes and encodesthe geometry information and the attribute information based on thedetermined QP values for geometry information and attribute information,respectively (S5305).

Note that although an example has been described in which the QP valuefor geometry information is determined based on the geometryinformation, and the QP value for attribute information is determinedbased on the attribute information, the present disclosure is notlimited thereto. For example, the QP values for geometry information andattribute information may be determined based on the geometryinformation, based on the attribute information, or based on thegeometry information and the attribute information.

Note that the QP values for geometry information and attributeinformation may be adjusted by considering the balance between thequality of the geometry information and the quality of the attributeinformation in the point cloud data. For example, the QP values forgeometry information and attribute information may be set in such amanner that the quality of the geometry information is high, and thequality of the attribute information is lower than the quality of thegeometry information. For example, the QP value for attributeinformation may be determined under a restriction that the QP value forattribute information is equal to or higher than the QP value forgeometry information.

The QP value may be adjusted so that encoded data is generated within apredetermined range of rate. For example, when the code amount of theencoding of the preceding data unit is expected to exceed apredetermined rate, that is, when the difference from a predeterminedrate is less than a first difference, the QP value may be adjusted todecrease the coding quality so that the difference between thepredetermined rate and the code amount of the data unit is less than thefirst difference. On the other hand, when the difference from thepredetermined rate is greater than a second difference, which is greaterthan the first difference, and there is a substantial difference, the QPvalue may be adjusted to improve the coding quality of the data unit.The adjustment between data units may be made between PCC frames orbetween tiles or slices. The adjustment of the QP value for attributeinformation may be made based on the rate of encoding of geometryinformation.

Note that, in the flowchart of FIG. 96 , the processing concerninggeometry information and the processing concerning attribute informationmay be performed in reverse order or in parallel.

Note that, although the flowchart of FIG. 96 shows a slice-based processas an example, a tile-based process or a process on a basis of otherdata units can be performed in the same manner as the slice-basedprocess. That is, slice in the flowchart of FIG. 96 can be replaced withtile or other data units.

FIG. 97 is a flowchart illustrating an example of a process of decodinggeometry information and attribute information.

As illustrated in FIG. 97 , the three-dimensional data decoding deviceobtains a reference value and difference information that indicate a QPvalue for geometry information, and a reference value and differenceinformation that indicate a QP value for attribute information (S5311).Specifically, the three-dimensional data decoding device analyzes one orboth of the transmitted metadata or the header of the transmittedencoded data, and obtains reference values and difference informationfor deriving the QP values.

The three-dimensional data decoding device then derives the QP valuesusing the obtained reference values and difference information in apredetermined manner.

The three-dimensional data decoding device then obtains quantizedgeometry information, and obtains geometry information byinverse-quantizing the quantized geometry information using the derivedQP value (S5313).

The three-dimensional data decoding device then obtains quantizedattribute information, and obtains attribute information byinverse-quantizing the quantized attribute information using the derivedQP value (S5314).

Next, a method of transmitting a quantization parameter will bedescribed.

FIG. 98 is a diagram for describing a first example of the method oftransmitting a quantization parameter. Part (a) of FIG. 98 shows anexample of a relationship between QP values.

In FIG. 98 , Q_(G) and Q_(A) denote an absolute value of a QP value usedfor the encoding of geometry information and an absolute value of a QPvalue used for the encoding of attribute information, respectively.Q_(G) is an example of a first quantization parameter used forquantizing geometry information on each of a plurality ofthree-dimensional points. Δ(Q_(A), Q_(G)) denotes difference informationthat indicates a difference between Q_(A) and Q_(G) used for derivingQ_(A). That is, Q_(A) is derived using Q_(G) and Δ(Q_(A), Q_(G)). Inthis way, a QP value is separated into a reference value (absolutevalue) and difference information (relative value) for transmission. Inthe decoding, a desired QP value is derived from the transmittedreference value and difference information.

For example, in part (a) of FIG. 98 , the absolute value Q_(G) and thedifference information Δ(Q_(A), Q_(G)) are transmitted, and in thedecoding, Q_(A) is derived by adding Δ(Q_(A), Q_(G)) to Q_(G) as shownby Equation G5 below.Q _(A) =Q _(G)+Δ(Q _(A) ,Q _(G))  (Equation G5)

With reference to parts (b) and (c) of FIG. 98 , a method oftransmitting QP values in a case where point cloud data includinggeometry information and attribute information is divided into sliceswill be described. Part (b) of FIG. 98 shows a first example of arelationship between a reference value and difference information foreach QP value. Part (c) of FIG. 98 shows a first example of an order oftransmission of QP values, geometry information, and attributeinformation.

For each piece of geometry information and each piece of attributeinformation, QP values are classified into QP values (frame QPs) inunits of PCC frames and QP values (data QPs) in units of data units. TheQP value used for the encoding determined in step S5301 in FIG. 96 is aQP value in units of data units.

Here, Q_(G), which is a QP value used for the encoding of geometryinformation in units of PCC frames, is used as a reference value, and aQP value in units of data units is generated and transmitted asdifference information that indicates the difference from Q_(G).

Q_(G): a QP value for the encoding of geometry information for a PCCframe, which is transmitted as a reference value “1.” using GPS.

Q_(A): a QP value for the encoding of attribute information for a PCCframe, which is transmitted as difference information “2.” using APS.

Q_(Gs1), Q_(Gs2): QP values for the encoding of geometry information ofslice data, which are transmitted as difference information “3.” and“5.” indicating a difference from Q_(G), respectively, using the headerof the encoded data of the geometry information.

Q_(As1), Q_(As2): QP values for the encoding of attribute information ofslice data, which are transmitted as difference information “4.” and“6.” indicating a difference from Q_(A), respectively, using the headerof the encoded data of the attribute information.

Note that information used for deriving a frame QP is described inmetadata (GPS, APS) associated with the frame, and information used forderiving a data QP is described in metadata (header of encoded data)associated with the data.

In this way, the data QP is generated and transmitted as differenceinformation indicating a difference from the frame QP. Therefore, thedata amount of the data QP can be reduced.

In each piece of encoded data, first decoder 5340 refers to metadataindicated by an arrow in part (c) of FIG. 98 , and obtains a referencevalue and difference information associated with the encoded data. Firstdecoder 5340 then derives a QP value corresponding to the encoded datato be decoded based on the obtained reference value and differenceinformation.

For example, first decoder 5340 obtains the reference information “1.”and the difference information “2.” and “6.” indicated by arrows in part(c) of FIG. 98 from the metadata or the header, and derives the QP valueof As₂ by adding the difference information “2.” and “6.” to thereference information “1.” as shown by Equation G6 below.Q _(As2) =Q _(G)+Δ(Q _(A) ,Q _(G))+Δ(Q _(As2) ,Q _(A))  (Equation G6)

Point cloud data includes geometry information and zero or more piecesof attribute information. That is, point cloud data may include noattribute information or a plurality of pieces of attribute information.

For example, one three-dimensional point may have, as attributeinformation, color information, color information and reflectanceinformation, or one or more pieces of color information linked to one ormore pieces of point-of-view information.

Here, an example of a case where one three-dimensional point has twopieces of color information and reflectance information will bedescribed with reference to FIG. 99 . FIG. 99 is a diagram fordescribing a second example of the method of transmitting a quantizationparameter. Part (a) of FIG. 99 is a diagram illustrating a secondexample of the relationship between a reference value and differenceinformation for each QP value. Part (b) of FIG. 99 is a diagramillustrating a second example of the order of transmission of QP values,geometry information, and attribute information.

Q_(G) is an example of the first quantization parameter as in FIG. 98 .

Two pieces of color information are indicated by luminance (luma) Y andchrominances (chroma) Cb, Cr, respectively. Q_(Y1), which a QP valueused for the encoding of luminance Y1 of a first color, is derived fromQ_(G), which is a reference value, and Δ(Q_(Y1), Q_(G)), which indicatesthe difference between Q_(Y1) and Q_(G). Luminance Y1 is an example of afirst luminance, and Q_(Y1) is an example of a second quantizationparameter used for quantizing luminance Y1 as the first luminance.Δ(Q_(Y1), Q_(G)) is difference information “2.”.

Q_(Cb1) and Q_(Cr1), which are QP values used for the encoding ofchrominances Cb1 and Cr1 of the first color, are derived from Q_(Y1) andΔ(Q_(Cb1), Q_(Y1)) and Δ(Q_(Cr1), Q_(Y1)), which indicate the differencebetween Q_(Cb1) and Q_(Y1) and the difference between Q_(Cr1) andQ_(Y1), respectively. Chrominances Cb1 and Cr1 are examples of a firstchrominance, and Q_(Cb1) and Q_(Cr1) are examples of a thirdquantization parameter used for quantizing chrominances Cb1 and Cr1 asthe first chrominance. Δ(Q_(Cb1), Q_(Y1)) is difference information“3.”, and Δ(Q_(Cr1), Q_(Y1)) is difference information “4.”. Δ(Q_(Cb1),Q_(Y1)) and Δ(Q_(Cr1), Q_(Y1)) are examples of a first difference.

Note that Q_(Cb1) and Q_(Cr1) may be identical values or a common value.When a common value is used, one of Q_(Cb1) and Q_(Cr1) can be used, andthe other can be omitted.

Q_(Y1D), which is a QP value used for the encoding of luminance Y1D ofthe first color in the slice data, is derived from Q_(Y1) and Δ(Q_(Y1D),Q_(Y1)) indicating the difference between Q_(Y1D) and Q_(Y1). LuminanceY1D of the first color in the slice data is an example of the firstluminance of one or more three-dimensional points included in thesubspace, and Q_(Y1D) is an example of a fifth quantization parameterused for quantizing luminance Y_(1D). Δ(Q_(Y1D), Q_(Y1)) is differenceinformation “10.”, and an example of a second difference.

Similarly, Q_(Cb1D) and Q_(Cr1D), which are QP values used for theencoding of chrominances Cb1D and Cr1D of the first color in the slicedata, are derived from Q_(Cb1) and Δ(Q_(Cb1D), Q_(Cb1)) indicating thedifference between Q_(Cb1D) and Q_(Cb1) and Q_(Cr1) and Δ(Q_(Cr1D),Q_(Cr1)) indicating the difference between Q_(Cr1D) and Q_(Cr1),respectively. Chrominances Cb1D and Cr1D of the first color in the slicedata are examples of the first chrominance of one or morethree-dimensional points included in the subspace, and Q_(Cb1D) andQ_(Cr1D) are examples of a sixth quantization parameter used forquantizing chrominances Cb1D and Cr1D. Δ(Q_(Cb1D), Q_(Cb1)) isdifference information “11.”, and Δ(Q_(Cr1D), Q_(Cr1)) is differenceinformation “12.”. Δ(Q_(Cb1D), Q_(Cb1)) and Δ(Q_(Cr1D), Q_(Cr1)) areexamples of a third difference.

The relationship between QP values for the first color holds for asecond color, so that redundant descriptions will be omitted.

Q_(R), which is a QP value used for the encoding of reflectance R, isderived from Q_(G), which is a reference value, and Δ(Q_(R), Q_(G)),which indicates the difference between Q_(R) and Q_(G). Q_(R) is anexample of a fourth quantization parameter used for quantizingreflectance R. Δ(Q_(R), Q_(G)) is difference information “8.”.

Q_(RD), which is a QP value used for the encoding of reflectance RD inthe slice data, is derived from Q_(R) and Δ(Q_(RD), Q_(R)), whichindicates the difference between Q_(RD) and Q_(R). Δ(Q_(RD), Q_(R)) isdifference information “16.”.

As described above, difference information “9.” to “16.” indicates thedifference between a data QP and a frame QP.

Note that when the values of the data QP and the frame QP are the same,for example, the difference information may be set at 0, or may not betransmitted, and the absence of the transmission may be regarded asdifference information of 0.

When obtaining chrominance Cr2 of the second color by decoding, forexample, first decoder 5340 obtains reference information “1.” anddifference information “5.”, “7.”, and “15.” indicated by arrows in part(b) of FIG. 99 from the metadata or the header, and derives the QP valueof chrominance Cr2 by adding difference information “5.”, “7.”, and“15.” to reference information “1.” as shown by Equation G7 below.Q _(Cr2D) =Q _(G)+Δ(Q _(Y2) ,Q _(G))+Δ(Q _(Cr2) ,Q _(Y2))+Δ(Q _(Cr2D) ,Q_(Cr2))  (Equation G7)

Next, an example of a case where geometry information and attributeinformation are divided into two tiles and then divided into two sliceswill be described with reference to FIG. 100 . FIG. 100 is a diagram fordescribing a third example of the method of transmitting a quantizationparameter. Part (a) of FIG. 100 shows a third example of therelationship between a reference value and difference information foreach QP value. Part (b) of FIG. 100 shows a third example of the orderof transmission of QP values, geometry information, and attributeinformation. Part (c) of FIG. 100 describes an intermediate generatedvalue for difference information in the third example.

When geometry information and attribute information are divided into aplurality of tiles and then further divided into a plurality of slices,as illustrated in part (c) of FIG. 100 , after the attribute informationis divided into tiles, a QP value (Q_(At1)) and difference informationΔ(Q_(At1), Q_(A)) for each tile are generated as intermediate generatedvalues. After the tile is divided into slices, QP values (Q_(At1s1),Q_(At1s2)) and difference information (Δ(Q_(At1s1), Q_(At1)),Δ(Q_(At1s2), Q_(At1))) are generated for each slice.

In this case, difference information “4.” in part (a) of FIG. 100 isderived according to Equation G8 below.Δ(Q _(At1s1) ,Q _(A))=Δ(Q _(At1) ,Q _(A))+Δ(Q _(At1s1) ,Q_(At1))  (Equation G8)

When obtaining attribute information A_(t2s1) for slice 1 in tile 2 bydecoding, for example, first decoder 5340 obtains reference information“1.” and difference information “2.” and “8.” indicated by arrows inpart (b) of FIG. 100 from the metadata or the header, and derives the QPvalue of attribute information At₂s₁ by adding difference information“2.” and “8.” to reference information “1.” as shown by Equation G9below.Q _(At2s1) =Q _(G)+Δ(Q _(At2s1) ,Q _(A))+Δ(Q _(A) ,Q _(G))  (EquationG9)

Next, a flow of a process of encoding point cloud data and a flow of aprocess of decoding point cloud data according to the present embodimentwill be described. FIG. 101 is a flowchart of a process of encodingpoint cloud data according to the present embodiment.

First, the three-dimensional data encoding device determines a divisionmethod to be used (S5321). The division method includes a determinationof whether to perform tile division or not and a determination ofwhether to perform slice division or not. The division method mayinclude the number of tiles or slices in the case where tile division orslice division is performed, and the type of division, for example. Thetype of division is a scheme based on an object shape, a scheme based onmap information or geometry information, or a scheme based on a dataamount or processing amount, for example. Note that the division methodmay be determined in advance.

When tile division is to be performed (if Yes in S5322), thethree-dimensional data encoding device generates a plurality of piecesof tile geometry information and a plurality of pieces of tile attributeinformation by dividing the geometry information and the attributeinformation on a tile basis (S5323). The three-dimensional data encodingdevice also generates tile additional information concerning the tiledivision.

When slice division is to be performed (if Yes in S5324), thethree-dimensional data encoding device generates a plurality of piecesof divisional geometry information and a plurality of pieces ofdivisional attribute information by dividing the plurality of pieces oftile geometry information and the plurality of pieces of tile attributeinformation (or the geometry information and the attribute information)(S5325). The three-dimensional data encoding device also generatesgeometry slice additional information and attribute slice additionalinformation concerning the slice division.

The three-dimensional data encoding device then generates a plurality ofpieces of encoded geometry information and a plurality of pieces ofencoded attribute information by encoding each of the plurality ofpieces of divisional geometry information and the plurality of pieces ofdivisional attribute information (S5326). The three-dimensional dataencoding device also generates dependency information.

The three-dimensional data encoding device then generates encoded data(encoded stream) by integrating (multiplexing) the plurality of piecesof encoded geometry information, the plurality of pieces of encodedattribute information and the additional information into a NAL unit(S5327). The three-dimensional data encoding device also transmits thegenerated encoded data.

FIG. 102 is a flowchart illustrating an example of a process ofdetermining a QP value and updating additional information in the tiledivision (S5323) and the slice division (S5325).

In steps S5323 and S5325, tile geometry information and tile attributeinformation and/or slice geometry information and slice attributeinformation may be independently divided in respective manners, or maybe collectively divided in a common manner. In this way, additionalinformation divided on a tile basis and/or on a slice basis isgenerated.

In these steps, the three-dimensional data encoding device determines areference value and difference information for a QP value on adivisional tile basis and/or on a divisional slice basis (S5331).Specifically, the three-dimensional data encoding device determinesreference value and difference information such as those illustrated inFIGS. 98 to 100 .

The three-dimensional data encoding device then updates the additionalinformation to include the determined reference value and differenceinformation (S5332).

FIG. 103 is a flowchart illustrating an example of a process in encoding(S5326).

The three-dimensional data encoding device encodes each of the pluralityof pieces of divisional geometry information and the plurality of piecesof divisional attribute information (S5341). Specifically, thethree-dimensional data encoding device encodes each of the plurality ofpieces of divisional geometry information and the plurality of pieces ofdivisional attribute information using the determined QP value.

The three-dimensional data encoding device then continues the encodingprocess until a condition for stopping the encoding process issatisfied, such as until there is no data to be encoded (S5342).

FIG. 104 is a flowchart illustrating a process of decoding point clouddata according to the present embodiment. First, the three-dimensionaldata decoding device determines the division method by analyzingadditional information (tile additional information, geometry sliceadditional information, and attribute slice additional information)concerning the division method included in encoded data (encoded stream)(S5351). The division method includes a determination of whether toperform tile division or not and a determination of whether to performslice division or not. The division method may include the number oftiles or slices in the case where tile division or slice division isperformed, and the type of division, for example.

The three-dimensional data decoding device then generates divisionalgeometry information and divisional attribute information by decoding aplurality of pieces of encoded geometry information and a plurality ofpieces of encoded attribute information included in the encoded datausing dependency information included in the encoded data (S5352).

If the additional information indicates that slice division has beenperformed (if Yes in S5353), the three-dimensional data decoding devicegenerates a plurality of pieces of tile geometry information and aplurality of pieces of tile attribute information by combining theplurality of pieces of divisional geometry information and the pluralityof pieces of divisional attribute information based on the geometryslice additional information and the attribute slice additionalinformation (S5354).

If the additional information indicates that tile division has beenperformed (if Yes in S5355), the three-dimensional data decoding devicegenerates geometry information and attribute information by combiningthe plurality of pieces of tile geometry information and the pluralityof pieces of tile attribute information (the plurality of pieces ofdivisional geometry information and the plurality of pieces ofdivisional attribute information) based on the tile additionalinformation (S5356).

FIG. 105 is a flowchart illustrating an example of a process ofobtaining QP values and decoding a QP value for a slice or tile in thecombining of information divided into slices (S5354) and the combiningof information divided into tiles (S5356).

Pieces of slice geometry information and pieces of slice attributeinformation or pieces of tile geometry information or pieces of tileattribute information may be combined in respective manners or in thesame manner.

The three-dimensional data decoding device obtains the reference valueand the difference information by decoding the additional information inthe encoded stream (S5361).

The three-dimensional data decoding device then calculates aquantization value using the decoded reference value and differenceinformation, and updates the QP value used for inverse quantization tothe calculated QP value (S5362). In this way, a QP value for inversequantization of quantized attribute information for each tile or slicecan be derived.

The three-dimensional data decoding device then continues the decodingprocess until a condition for stopping the decoding process issatisfied, such as until there is no data to be decoded (S5363).

FIG. 106 is a diagram illustrating a syntax example of GPS. FIG. 107 isa diagram illustrating a syntax example of APS. FIG. 108 is a diagramillustrating a syntax example of a header of geometry information. FIG.109 is a diagram illustrating a syntax example of a header of attributeinformation.

As illustrated in FIG. 106 , for example, GPS, which is additionalinformation of geometry information, includes QP_value, which indicatesan absolute value used as a reference for deriving a QP value. QP_valuecorresponds to Q_(G) illustrated in FIGS. 98 to 100 .

As illustrated in FIG. 107 , for example, when a three-dimensional pointhas a plurality of pieces of color information associated with aplurality of points of view, APS, which is additional information ofattribute information, may define a default point of view, and a 0-thpiece of attribute information may always describe information on thedefault point of view. For example, when decoding or displaying a singlepiece of color information, the three-dimensional data encoding devicecan decode or display the 0-th piece of attribute information.

APS includes QP_delta_Attribute_to_Geometry.QP_delta_Attribute_to_Geometry is difference information indicating thedifference from the reference value (QP_value) described in GPS. Thedifference information indicates a difference in luminance when theattribute information is color information, for example.

GPS may include a flag that indicates whether or not Geometry_header(header of the geometry information) includes difference informationused for calculating a QP value. APS may include a flag that indicateswhether or not Attribute_header (header of the attribute information)includes difference information used for calculating a QP value. Theflag may indicate whether or not the attribute information includesdifference information indicating the difference of a data QP from aframe QP, which is used for calculating the data QP.

When a first color of attribute information is indicated by a firstluminance and a first chrominance, in the quantization of the firstluminance using a second quantization parameter and the quantization ofthe first chrominance using a third quantization parameter, if thequantizations are performed using a fifth quantization parameter and asixth quantization parameter, the encoded stream may includeidentification information (flag) that indicates that the quantizationsare performed using the fifth quantization parameter and the sixthquantization parameter.

As illustrated in FIG. 108 , the header of the geometry information mayinclude QP_delta_data_to_frame, which is difference informationindicating the difference from the reference value (QP_value) describedin GPS. The header of the geometry information may be divided intopieces of information associated with tiles and/or slices, and aQP_value corresponding to each tile and/or slice may be indicated.

As illustrated in FIG. 109 , the header of the attribute information mayinclude QP_delta_data_to_frame, which is difference informationindicating the difference from the QP value described in APS.

Although the reference value of a QP value has been described as being aQP value of geometry information for a PCC frame with reference to FIGS.98 to 100 , the present disclosure is not limited thereto, and othervalues may be used as a reference value.

FIG. 110 is a diagram for describing another example of the method oftransmitting a quantization parameter.

Parts (a) and (b) of FIG. 110 illustrate a fourth example, in whichcommon reference value Q is set based on QP values of geometryinformation and attribute information for a PCC frame. In the fourthexample, reference value Q is stored in GPS, difference informationindicating the difference of a QP value (Q_(G)) of geometry informationfrom reference value Q is stored in GPS, and difference informationindicating the differences of QP values (Q_(Y) and Q_(R)) of attributeinformation from reference value Q is stored in APS. Note that referencevalue Q may be stored in SPS.

Parts (c) and (d) of FIG. 110 illustrate a fifth example, in which adifferent reference value is set for each of geometry information andattribute information. In the fifth example, reference QP values(absolute values) of geometry information and attribute information arestored in GPS and APS, respectively. That is, reference value Q_(G) isset in geometry information, reference value Q_(Y) is set in colorinformation of attribute information, and reference value Q_(R) is setas reflectance of attribute information. In this way, a reference valueof a QP value may be set for each of geometry information and aplurality of kinds of attribute information. Note that the fifth examplemay be combined with another example. That is, Q_(A) in the firstexample, or Q_(Y1), Q_(Y2), and Q_(R) in the second example may be areference value of a QP value.

Parts (e) and (f) of FIG. 110 illustrate a sixth example, in which whenthere is a plurality of PCC frames, a common reference value Q is setfor the plurality of PCC frames. In the sixth example, reference value Qis stored in SPS or GPS, and difference information indicating thedifference between the QP value of the geometry information and thereference value for each PCC frame is stored in GPS. Note that, withinthe range of a random access unit, such as GOF, for example, the leadingframe of the random access unit may be designated as a reference value,and difference information Δ(Q_(G(1)), Q_(G(0))) indicating thedifferences between the PCC frames may be transmitted.

Note that, even when a tile or a slice is further divided, differenceinformation indicating the difference from the QP value of the unit ofdivision is stored in the data header and transmitted in the samemanner.

FIG. 111 is a diagram for describing another example of the method oftransmitting a quantization parameter.

Parts (a) and (b) of FIG. 111 illustrate a seventh example, in whichcommon reference value Q_(G) is set for geometry information andattribute information of a PCC frame. In the seventh example, referencevalue Q_(G) is stored in GPS, and difference information indicating thedifferences from the geometry information or the attribute informationis stored in the respective data headers. Reference value Q_(G) may bestored in SPS.

Parts (c) and (d) of FIG. 111 shows an eighth example, in which a QPvalue of attribute information is indicated by difference informationindicating the difference from a QP value of geometry informationbelonging to the same slice and tile. In the eighth example, referencevalue Q_(G) may be stored in SPS.

FIG. 112 is a diagram for describing a ninth example of the method oftransmitting a quantization parameter.

Parts (a) and (b) illustrate the ninth example, in which a plurality ofpieces of attribute information has a common QP value, and each piece ofattribute information indicates difference information indicating thedifference between the common QP value and the QP value of geometryinformation and difference information indicating the difference fromthe value of the attribute information and the common QP value.

FIG. 113 is a diagram for describing an example of control of a QPvalue.

As the value of the quantization parameter decreases, the qualityimproves, while the coding efficiency decreases because more bits arerequired.

For example, when encoding three-dimensional point cloud data bydividing the three-dimensional point cloud data into tiles, if pointcloud data included in a tile is a main road, the point cloud data isencoded using a previously defined QP value of attribute information. Onthe other hand, peripheral tiles do not include important information,and therefore, the coding efficiency may be able to be improved bysetting the difference information of the QP value at a positive valueto reduce the quality of the data.

Furthermore, when encoding the three-dimensional point cloud datadivided into tiles by dividing the tiles into slices, a sidewalk, atree, and a building are important for positional estimation(localization and mapping) in automatic driving, so that the QP value isset at a negative value. On the other hand, a moving body and otherobjects are less important, so that the QP value is set at a positivevalue.

Part (b) of FIG. 113 shows an example in which difference information isderived in a case where a quantization delta value is set in advancebased on the object included in a tile or slice. For example, whendivisional data is slice data on a “building” included in a tile of a“main road”, the difference information is −5, which is derived bysumming the quantization delta value of 0 of the tile of a “main road”and the quantization delta value of −5 of the slice data on a“building”.

FIG. 114 is a flowchart illustrating an example of a method ofdetermining a QP value based on the quality of an object.

The three-dimensional data encoding device divides point cloud data intoone or more tiles based on map information, and determines an objectincluded in the one or more tiles (S5371). Specifically, thethree-dimensional data encoding device performs an object recognitionprocess for recognizing what an object is using a leaning model obtainedby machine learning, for example.

The three-dimensional data encoding device then determines whether toencode a tile to be processed with high quality or not (S5372). Toencode with high quality means encoding at a bitrate higher than apredetermined rate, for example.

When the tile to be processed is to be encoded with high quality (if Yesin S5372), the three-dimensional data encoding device then sets the QPvalue of the tile so that the coding efficiency is high (S5373).

On the other hand, when the tile to be processed is not to be encodedwith high quality (if No in S5372), the three-dimensional data encodingdevice sets the QP value of the tile so that the coding efficiency islow (S5374).

Following step S5373 or S5374, the three-dimensional data encodingdevice determines the object in the tile, and divides the tile into oneor more slices (S5375).

The three-dimensional data encoding device then determines whether toencode a slice to be processed with high quality or not (S5376).

When the slice to be processed is to be encoded with high quality (ifYes in S5376), the three-dimensional data encoding device then sets theQP value of the slice so that the coding efficiency is high (S5377).

On the other hand, when the slice to be processed is not to be encodedwith high quality (if No in S5376), the three-dimensional data encodingdevice sets the QP value of the slice so that the coding efficiency islow (S5378).

The three-dimensional data encoding device then determines a referencevalue and difference information to be transmitted based on the set QPvalue in a predetermined manner, and stores the determined referencevalue and difference information in at least one of the additionalinformation and the header of the data (S5379).

The three-dimensional data encoding device then quantizes and encodesthe geometry information and the attribute information based on thedetermined QP value (S5380).

FIG. 115 is a flowchart illustrating an example of a method ofdetermining a QP value based on a rate control.

The three-dimensional data encoding device sequentially encodes pointcloud data (S5381).

The three-dimensional data encoding device then determines a ratecontrol status concerning the encoding process from the code amount ofthe encoded data and the occupancy of an encoding buffer, and determinesthe quality of the subsequent encoding (S5382).

The three-dimensional data encoding device then determines whether ornot to increase the encoding quality (S5383).

When the encoding quality is to be increased (if Yes in S5383), thethree-dimensional data encoding device sets the QP value of the tile sothat the encoding quality is higher (S5384).

On the other hand, when the encoding quality is not to be increased (ifNo in S5383), the three-dimensional data encoding device sets the QPvalue of the tile so that the encoding quality is lower (S5385).

The three-dimensional data encoding device then determines a referencevalue and difference information to be transmitted based on the set QPvalue in a predetermined manner, and stores the determined referencevalue and difference information in at least one of the additionalinformation and the header of the data (S5386).

The three-dimensional data encoding device then quantizes and encodesthe geometry information and the attribute information based on thedetermined QP value (S5387).

As described above, the three-dimensional data encoding device accordingto the present embodiment performs the process illustrated in FIG. 116 .First, the three-dimensional data encoding device quantizes geometryinformation on each of a plurality of three-dimensional points using afirst quantization parameter (S5391). The three-dimensional dataencoding device quantizes a first luminance using a second quantizationparameter and quantizes a first chrominance using a third quantizationparameter, the first luminance and the first chrominance indicating afirst color among attribute information on each of the plurality ofthree-dimensional points (S5392). The three-dimensional data encodingdevice generates a bitstream including the quantized geometryinformation, the quantized first luminance, the quantized firstchrominance, the first quantization parameter, the second quantizationparameter, and a first difference between the second quantizationparameter and the third quantization parameter (S5393).

With such a configuration, since the third quantization parameter isindicated by the first difference from the second quantization parameterin the bitstream, the coding efficiency can be improved.

For example, the three-dimensional data encoding device furtherquantizes a reflectance among the attribute information on each of theplurality of three-dimensional points using a fourth quantizationparameter. Furthermore, in the generation described above, the bitstreamgenerated further includes the quantized reflectance and the fourthquantization parameter.

For example, in the quantization using the second quantizationparameter, for each of a plurality of subspaces obtained by dividing acurrent space including the plurality of three-dimensional points, thefirst luminance of one or more three-dimensional points included in thesubspace is quantized further using a fifth quantization parameter. Inthe quantization using the third quantization parameter, the firstchrominance of the one or more three-dimensional points is quantizedfurther using a sixth quantization parameter. In the generationdescribed above, the bitstream generated further includes a seconddifference between the second quantization parameter and the fifthquantization parameter and a third difference between the thirdquantization parameter and the sixth quantization parameter.

With such a configuration, since the fifth quantization parameter isindicated by the second difference from the second quantizationparameter and the sixth quantization parameter is indicated by the thirddifference from the third quantization parameter in the bitstream, thecoding efficiency can be improved.

For example, in the generating described above, the bitstream generatedfurther includes identification information indicating that the fifthquantization parameter and the sixth quantization parameter have beenused in the quantization using the second quantization parameter and thequantization using the third quantization parameter, respectively.

With such a configuration, the three-dimensional data decoding devicehaving obtained the bitstream can determine from the identificationinformation that the quantization using the fifth quantization parameterand the quantization using the sixth quantization parameter have beenperformed, so that the processing load of the decoding process can bereduced.

For example, the three-dimensional data encoding device furtherquantizes a second luminance using a seventh quantization parameter andquantizes a second chrominance using an eighth quantization parameter,the second luminance and the second chrominance indicating a secondcolor among the attribute information of each of the plurality ofthree-dimensional points. In the generation described above, thebitstream generated further includes the quantized second luminance, thequantized second chrominance, the seventh quantization parameter, and afourth difference between the seventh quantization parameter and theeighth quantization parameter.

With such a configuration, since the eighth quantization parameter isindicated by the fourth difference from the seventh quantizationparameter in the bitstream, the coding efficiency can be improved. Inaddition, two types of color information can be included in theattribute information on a three-dimensional point.

For example, the three-dimensional data encoding device includes aprocessor and memory, and the processor performs the process describedabove using the memory.

The three-dimensional data decoding device according to the presentembodiment performs the process illustrated in FIG. 117 . First, thethree-dimensional data decoding device obtains quantized geometryinformation, a quantized first luminance, a quantized first chrominance,a first quantization parameter, a second quantization parameter, and afirst difference between the second quantization parameter and a thirdquantization parameter, by obtaining a bitstream (S5394). Thethree-dimensional data decoding device calculates geometry informationon a plurality of three-dimensional points by inverse-quantizing thequantized geometry information using the first quantization information(S5395). Of a first luminance and a first chrominance indicating a firstcolor of the plurality of three-dimensional points, thethree-dimensional data decoding device calculates the first luminance byinverse-quantizing the quantized first luminance using the secondquantization parameter (S5396). The three-dimensional data decodingdevice calculates the first chrominance by inverse-quantizing thequantized first chrominance using the third quantization parameterobtained from the second quantization parameter and the first difference(S5397).

In this way, the three-dimensional data decoding device can correctlydecode geometry information and attribute information on athree-dimensional point.

For example, in the obtaining, a quantized reflectance and a fourthquantization parameter are further obtained by obtaining the bitstream.The three-dimensional data decoding device further calculates areflectance of the plurality of three-dimensional points byinverse-quantizing the quantized reflectance using the fourthquantization parameter.

Therefore, the three-dimensional data decoding device can correctlydecode the reflectance of a three-dimensional point.

For example, in the obtaining, a second difference between the secondquantization parameter and a fifth quantization parameter and a thirddifference between the third quantization parameter and a sixthquantization parameter are further obtained by obtaining the bitstream.In the calculating of the first luminance, a first luminance of one ormore three-dimensional points is calculated by inverse-quantizing thequantized first luminance using the second quantization parameter andthe fifth quantization parameter obtained from the second difference,the one or more three-dimensional points being included in each subspaceobtained by dividing a current space including the plurality ofthree-dimensional points, the quantized first luminance being theluminance obtained by quantizing the first luminance of the one or morethree-dimensional points using the second quantization parameter and thefifth quantization parameter. In the calculation of the firstchrominance, a first chrominance of the one or more three-dimensionalpoints is calculated by inverse-quantizing the quantized firstchrominance using the third quantization parameter and the sixthquantization parameter obtained from the third difference, the quantizedfirst chrominance being the chrominance obtained by quantizing the firstchrominance of the at least one three-dimensional point using the thirdquantization parameter and the sixth quantization parameter.

For example, in the obtaining, identification information indicatingthat the quantization using the fifth quantization parameter and thequantization using the sixth quantization parameter have been performedis further obtained by obtaining the bitstream. In the calculation ofthe first luminance, when the identification information indicates thatthe quantization using the fifth quantization parameter and thequantization using the sixth quantization parameter have been performed,the quantized first luminance is determined to be a luminance obtainedby quantizing the first luminance of the one or more three-dimensionalpoints. In the calculation of the first chrominance, when theidentification information indicates that the quantization using thefifth quantization parameter and the quantization using the sixthquantization parameter have been performed, the quantized firstchrominance is determined to be a chrominance obtained by quantizing thefirst chrominance of the one or more three-dimensional points.

With such a configuration, the three-dimensional data decoding devicecan determine from the identification information that the quantizationusing the fifth quantization parameter and the quantization using thesixth quantization parameter have been performed, so that the processingload of the decoding process can be reduced.

For example, in the obtaining, a quantized second luminance, a quantizedsecond chrominance, a seventh quantization parameter, and a fourthdifference between the seventh quantization parameter and an eighthquantization parameter is further obtained by obtaining the bitstream.Of a second luminance and a second chrominance that indicate a secondcolor of the plurality of three-dimensional points, thethree-dimensional data decoding device further calculates the secondluminance by inverse-quantizing the quantized second luminance using theseventh quantization parameter. The three-dimensional data decodingdevice further calculates the second chrominance by inverse-quantizingthe quantized second chrominance using the eighth quantization parameterobtained from the seventh quantization parameter and the fourthdifference.

In this way, the three-dimensional data decoding device can correctlydecode the second color of a three-dimensional point.

For example, the three-dimensional data decoding device includes aprocessor and memory, and the processor performs the process describedabove using the memory.

Embodiment 12

With the three-dimensional data encoding method, the three-dimensionaldata decoding method, the three-dimensional data encoding device, andthe three-dimensional data decoding device described with regard toEmbodiment 8, the processes according to this embodiment described beloware also possible.

FIG. 118 is a diagram for illustrating an example of a quantizationparameter transmission method according to the present embodiment. Part(a) of FIG. 118 shows an example in which a reference value for the QPvalue is set for each of geometry information and attribute information.FIG. 118 mainly differs from FIG. 105 showing Embodiment 8 in that areference value for the QP value is set not only for geometryinformation but also for attribute information. That is, a QP value forat least one of a plurality of pieces of attribute information includinga first color, a second color, and a reflectance is designated as areference value, and QP values for other attribute information areindicates as difference information with respect to the common referencevalue.

In FIG. 118 , Q_(Y1), which is a QP value used for encoding of luminanceY1 of the first color, is set as a common reference value for aplurality of pieces of attribute information including the first color,the second color, and the reflectance. Q_(Y2), which is a referencevalue for the second color, is derived using common reference valueQ_(Y1) and Δ(Q_(Y2), Q_(Y1)), which is difference information “5.” withrespect to Q_(Y1). Q_(R), which is a reference value for reflectance, isderived using common reference value Q_(Y1) and Δ(Q_(R), Q_(Y1)), whichis difference information “8.” with respect to Q_(Y1). In this case,common reference value Q_(Y1) is included in APS1, which is APS for thefirst color.

For a fourth attribute, a different reference value than commonreference value Q_(Y1) may be set. A fifth attribute may have no QPvalue. That is, the attribute information may include both attributeinformation that is quantized using a common reference value forderiving a plurality of QP values used for quantization of a pluralityof pieces of attribute information and attribute information that isquantized using a different reference value than the common referencevalue. The attribute information may further include attributeinformation that is encoded without using a QP value.

Note that, although an example where the QP value used for quantizationof the attribute information of the first color is a common referencevalue for deriving QP values used for quantization of a plurality ofpieces of attribute information has been described with reference toFIG. 118 , the common reference value may be determined according to therules described below. For example, when all attribute information isdescribed in control information such as SPS, the QP value included inthe first attribute information indicated in SPS among all the attributeinformation may be designated as a common reference value.Alternatively, the control information such as SPS may indicateattribute information that is to be quantized using a QP valuedesignated as a common reference value. Alternatively, in the controlinformation such as SPS, the attribute information that is to bequantized using a QP value designated as a common reference value may beindicated first among the plurality of pieces of attribute information.In any case, by representing the QP value used for quantization of eachof the plurality of pieces of attribute information as a combination ofa reference value and difference information, the amount of encoded datacan be reduced.

Note that different reference values Q_(Y1), Q_(Y2), and Q_(R) fordifferent pieces of attribute information may be indicated in APS,Q_(Y1) may be used as a reference value for the QP value for the firstcolor, Q_(Y2) may be used as a reference value for the QP value for thesecond color, and Q_(R) may be used as a reference value for the QPvalue for the reflectance. In that case, Q_(Y2) and Q_(R) arerepresented by an absolute value, as with Q_(Y1).

A first example is a method of indicating a QP value for a plurality ofpieces of attribute information when metadata for the plurality ofpieces of attribute information is collectively described in one APS.

FIG. 119 is a diagram showing a first example of a syntax of APS and asyntax of a header of attribute information.

First, a syntax example of APS will be described.

aps_idx denotes an index number of APS. aps_idx indicates acorrespondence between APS and a header of attribute information.

sps_idx denotes an index number of SPS to which APS corresponds.

num_of_attribute denotes the number of pieces of attribute information.Note that, when APS is set for each piece of attribute information, afield or loop of num_of_attribute need not be included in APS.

attribute_type denotes the type of attribute information or, in otherwords, the kind of attribute information. Note that, when the type ofattribute information is described in corresponding SPS, informationthat allows reference to the type of attribute information described inSPS may be included in APS instead of attribute_type.

In FIG. 119 , the if sentence enclosed by dashed line 6701 indicates aQP value depending on attribute_type. For example, when the type ofattribute information is color, the QP value for the luminance (luma)represented by an absolute value is indicated as a reference value, andthe QP values for the chrominance (chroma: Cb, Cr) are indicated asdifference information with respect to the QP value for the luminance.

On the other hand, when the type of attribute information isreflectance, the QP value for the reflectance represented by an absolutevalue is indicated. As another example, when the type of attributeinformation has no QP value, no QP value is indicated.

When there are two or more pieces of attribute information, thereference value (QP_value_Luma or QP_value in this example) for a pieceof attribute information may be indicated by the difference from thereference value for another piece of attribute information. For example,in the loop of num_of_attribute, a reference value for common attributeinformation may be indicated when i=0, and a difference value withrespect to the common attribute information may be indicated when i=>1.

data_QP_delata_present_flag is a flag that indicates whether a QP valuefor each piece of data (slice) is present in the header of the attributeinformation. When the flag is 1, a QP value for each piece of data(slice) is indicated in the header of the attribute information.

Next, a syntax example of the header of the attribute information willbe described.

The header of the attribute information also includes aps_idx.Therefore, a correspondence between APS and the header of the attributeinformation is indicated by APS and aps_idx included in the header ofthe attribute information. That is, the fact that APS and the header ofthe attribute information shares aps_idx indicates that there is acorrespondence between APS and the header of the attribute information.

attribute_type indicates the type of attribute information (kind ofattribute information). Note that when the type of attribute informationis described in the corresponding APS or SPS, information that allowsreference to the type of attribute information described in APS or SPSmay be included in the header of the attribute information, instead ofattribute_type.

The QP values for the fields in the if sentence enclosed by dashed line6702, specifically, QP_delata_data_to_frame, QP_delta1_to_frame, andQP_delta2_to_frame, are QP values for data corresponding toattribute_type. Each QP value indicates difference information withrespect to the value described in APS.

A second example is a method of indicating a QP value for attributeinformation when metadata for one piece of attribute information isindependently described in one APS. In the second example, various types(kinds) of attribute information have a common header structure, andtherefore, a change of the syntax structure with the attributeinformation can be advantageously avoided.

FIG. 120 is a diagram showing a second example of the syntax of APS.FIG. 121 is a diagram showing a second example of the syntax of theheader of attribute information.

APS includes a reference value and a difference value for a QP value ofa frame. When data_QP_delta_present_flag of APS is 1, the header of theattribute information includes difference information with respect tothe reference value for APS.

Here, the fields relating to QP values are always present, whether thetype of attribute information is color, reflectance, or frame number,for example. APS has a first number of fields for storing N QP values (Nis 2 or greater), regardless of the type of attribute information. Here,N is 3, for example.

When the type of attribute information is color, for example, QP_valuein APS stores information that indicates a QP value for luma, andQP_delta1 and QP_delta2 store information that indicates QP values forchroma. For example, QP_value is a reference value, and QP_delta1 andQP_delta2 are difference information with respect to QP_value. That is,the QP value for luma is indicated by QP_value, and the QP values forchroma are indicated by a value obtained by adding QP_delta1 to QP_valueand a value obtained by adding QP_delta2 to QP_value. In this way, APSincludes a reference value for a quantization parameter for quantizingcorresponding attribute information.

Similarly, QP_delta_data_to_frame in the header of attribute informationstores difference information on the QP value for luma with respect toQP_value in corresponding APS. QP_delta1_to_frame and QP_delta2_to_framemay store difference information on QP values for chroma with respect toQP_delta1 and QP_delta2 in corresponding APS, respectively.

When the type of attribute information is reflectance, for example,QP_value in APS may store information that indicates a QP value forreflectance, and QP_delta1 and QP_delta2 may store information thatalways indicates 0 or invalidity. Similarly, QP_delta_data_to_frame inthe header of attribute information may store information that indicatesa QP value for reflectance, and QP_delta1_to_frame andQP_delta2_to_frame may store information that always indicates 0 orinvalidity. In that case, the three-dimensional data decoding deviceneed not use for decoding and may ignore the information stored inQP_delta1 and QP_delta2 storing information that indicates 0 orinvalidity and QP_delta1_to_frame and QP_delta2_to_frame, regardless ofthe information.

As another example, when the type of attribute information has no QPvalue, all the fields in APS may store information that indicates 0 orinvalidity. In that case, data_AP_delta_present_flag is also set at 0.In that case, the three-dimensional data decoding device need not usefor decoding and may ignore the information stored in QP_delta1 andQP_delta2 storing information that indicates 0 or invalidity andQP_delta1_to_frame and QP_delta2_to_frame, regardless of theinformation. In this way, the three-dimensional data decoding device mayignore a parameter stored in a particular field of a plurality of fieldsin a header of particular attribute information that corresponds to aparticular kind of attribute in the headers of a plurality of pieces ofattribute information.

With such a configuration, QP values for different types of attributeinformation can be indicated by combinations of a reference value anddifference information in a common syntax structure, so that the codingefficiency can be improved.

Note that when attribute information corresponding to one piece ofgeometry information includes two or more pieces of color information,the way of indicating the attribute information may be changed dependingon the type of the attribute information. For example, the colorinformation may be indicated by a common QP reference value anddifference information, and a QP reference value for reflectance may beseparately indicated in APS.

The present invention is not limited to the methods described withregard to Embodiments 8 and the present embodiment, and the referencevalue may be signaled separately from the difference information, or thedifference information may be independently signaled as a referencevalue. For example, the combination of a reference value and differenceinformation may be adaptively changed depending on the properties ofdata. For example, at least one reference value may be transmitted for aunit that need to be independently decoded, and difference informationmay be transmitted for a unit that need not be independently decoded. Inthis way, the functionality can be improved, and at the same time thecode amount can be reduced.

Alternatively, the amount of information may be calculated forcombinations of a reference value and difference information, and acombination of a reference value and difference information that has theminimum amount of information may be generated and delivered based onthe result of the calculation. When adaptively changing the combinationof a reference value and difference information, the meaning (semantics)of the field that indicates the reference value and the field thatindicates the difference information may be adaptively changed. Forexample, the meaning of each field may be changed, such as bydetermining whether to set each field to be invalid or not according tothe rules described above, or a flag that indicates to change themeaning of each field may be added. Alternatively, the referencedestination for the reference value may be adaptively changed. In thatcase, a flag that indicates that the reference destination has beenchanged, or an Id or the like that allows identification of thereference destination may be indicated.

Next, with reference to FIG. 122 , a method of indicating a relationshipbetween attribute information described in SPS, APS, andAttribute_header (header of attribute information) by usingattribute_component_id will be described. FIG. 122 is a diagram showinga relationship between SPS, APS, and a header of attribute information.Note that the destination of the arrows in FIG. 122 indicates thereference destination.

SPS includes information concerning the types of a plurality of piecesof attribute information. That is, SPS may correspond to a plurality ofpieces of attribute information and include a plurality of pieces ofinformation attribute_type each of which indicates a different kind ofattribute information. SPS also includes, for each type of attributeinformation, attribute_component_id that indicates a number that allowsidentification of the type of attribute information. Note that SPS is anexample of control information. attribute_type is an example of typeinformation. attribute_component_id included in SPS is an example offirst identification information that indicates that first attributecontrol information is associated with one of a plurality of pieces oftype information.

APS or Attribute_header includes attribute_component_id that correspondsto attribute_component_id included in SPS. Note that APS is an exampleof second attribute control information. Attribute_header is an exampleof first attribute control information. attribute_component_id includedin APS is an example of second identification information that indicatesthat first attribute control information is associated with one of aplurality of pieces of type information.

The three-dimensional data decoding device refers to SPS indicated bysps_idx included in APS or Attribute_header. The three-dimensional datadecoding device then obtains the type of attribute informationcorresponding to attribute_component_id included in the APS orAttribute_header from the referred SPS as the type of attributeinformation to which the information included in the APS orAttribute_header corresponds. Note that one APS corresponds to one typeof attribute information. The header of one piece of attributeinformation corresponds to one type of attribute information. Each of aplurality of APSs corresponds to the header(s) of one or more pieces ofattribute information. That is, one APS corresponds to the header(s) ofone or more pieces of attribute information other than the header(s) ofone or more pieces of attribute information that correspond to anotherAPS.

When attribute_component_id=0, for example, the three-dimensional datadecoding device can obtain attribute information (such asattribute_type) that corresponds to attribute_component_id having thesame value, that is, a value of 0, from SPS.

Note that, instead of attribute_component_id, the sequence of the piecesof attribute information described in SPS may be described in SPS. Thatis, type information that indicates a plurality of kinds of attributeinformation may be stored (described) in SPS in a predeterminedsequence. In that case, attribute_component_id included in APS orAttribute_header indicates that the APS or Attribute_header includingattribute_component_id is associated with type information at a positionin the predetermined sequence.

Alternatively, the sequence of transmitted APSs or attribute informationmay be made to agree with the sequence of attribute informationdescribed in SPS, thereby allowing the three-dimensional data decodingdevice to derive the sequence of arrival of APSs or attributeinformation and refer to attribute information corresponding to thesequence of arrival. When point cloud data includes both attributeinformation whose APS or Attribute_header may or may not be presentdepending on the frame and attribute information whose APS orAttribute_header is always present regardless of the frame, theattribute information whose APS or Attribute_header is always presentregardless of the frame may be first transmitted, and then the attributeinformation whose APS or Attribute_header may or may not be presentdepending on the frame may be transmitted.

Note that, although a plurality of APSs each of which corresponds to aplurality of pieces of attribute information is shown in one frame inFIGS. 118 and 122 , one APS may be used, instead of the plurality ofAPSs. In that case, one APS includes attribute information-relatedinformation that corresponds to a plurality of pieces of attributeinformation.

aps_idx may include a sequence number that corresponds to a framenumber. A correspondence between APS and Attribute_header may beindicated in this way. Note that aps_idx may have a function ofattribute_component_id. This allows information on the whole sequenceconcerning one or more kinds of APSs or attribute information to bestored in SPS and to be referred to from each APS or Attribute_header.

Note that in order to allow determination of the kind (attribute_type)of the attribute information of APS or Attribute_header, attribute_typemay be directly included in APS or Attribute_header, or may be includedin a NAL unit header as a kind of the NAL unit.

In any case, the attribute information of APS or Attribute_header can beobtained, and the kind of the attribute of the attribute information canbe determined.

As stated above, the three-dimensional data encoding device according tothe present embodiment performs the process shown by FIG. 123 . First,the three-dimensional data encoding device encodes pieces of attributeinformation of respective three-dimensional points, using parameters(S6701). The three-dimensional data encoding device generates abitstream including the pieces of attribute information encoded, controlinformation, and pieces of first attribute control information (S6702).The control information corresponds to the pieces of attributeinformation and includes pieces of type information each indicating atype of different attribute information. Moreover, the pieces of firstattribute control information correspond one-to-one with the pieces ofattribute information. Each of the pieces of first attribute controlinformation includes first identification information indicating thatthe first attribute control information is associated with one of thepieces of type information.

With such a configuration, since a bitstream including the firstidentification information for identifying the type of the attributeinformation to which the first attribute control information correspondsis generated, the three-dimensional data decoding device having receivedthe bitstream can correctly and efficiently decode attribute informationon a three-dimensional point.

For example, the pieces of type information are stored in the controlinformation in a predetermined sequence. The first identificationinformation indicates that first attribute control information includingthe first identification information is associated with one of thepieces of type information that has an order in the predeterminedsequence.

With such a configuration, since type information is indicated in apredetermined sequence without information indicating the typeinformation, the amount of data of the bitstream can be reduced, and theamount of the transmitted bitstream can be reduced.

For example, the bitstream further includes pieces of second attributecontrol information corresponding to the pieces of attributeinformation. Each of the pieces of second attribute control informationincludes a reference value of a parameter used for encoding acorresponding one of the pieces of attribute information.

With such a configuration, since each of a plurality of pieces of secondattribute control information includes a reference value of a parameter,the attribute information to which the second attribute controlinformation corresponds can be encoded using the reference value. Withsuch a configuration, since the three-dimensional data decoding devicehaving received the bitstream can identify the type of the secondattribute information using the second identification information, thethree-dimensional data decoding device can correctly and efficientlydecode attribute information on a three-dimensional point.

For example, each of the pieces of first attribute control informationincludes difference information that is a difference from the referencevalue of the parameter. With such a configuration, the coding efficiencycan be improved.

For example, the bitstream further includes pieces of second attributecontrol information corresponding to the pieces of attributeinformation. Each of the pieces of second attribute control informationincludes second identification information indicating that the secondattribute control information is associated with one of the pieces oftype information.

With such a configuration, since a bitstream including the secondidentification information for identifying the type of the attributeinformation to which the second attribute control informationcorresponds is generated, it is possible to generate the bitstream thatcan correctly and efficiently decode attribute information on athree-dimensional point.

For example, each of the pieces of first attribute control informationincludes N fields in which N parameters are stored, N being greater thanor equal to 2. In specific first attribute control information among thepieces of first attribute control information, one of the N fieldsincludes a value indicating invalidity, the specific first attributecontrol information corresponding to a specific type of an attribute.

With such a configuration, since the three-dimensional data decodingdevice having received the bitstream can identify the type of the firstattribute information using the first identification information andomit the decoding process in the case of specific first attributecontrol information, the three-dimensional data decoding device cancorrectly and efficiently decode attribute information on athree-dimensional point.

For example, in the encoding, the pieces of attribute information arequantized using quantization parameters as the parameters.

With such a configuration, since a parameter is expressed using adifference from a reference value, it is possible to improve codingefficiency for quantization.

For example, the three-dimensional data encoding device includes aprocessor and memory, and the processor performs the above process usingthe memory.

The three-dimensional data decoding device according to the presentembodiment performs the process shown by FIG. 124 . First, thethree-dimensional data decoding device obtains pieces of attributeinformation encoded and parameters from a bitstream (S6711). Thethree-dimensional data decoding device decodes the pieces of attributeinformation encoded using the parameters, to generate pieces ofattribute information of respective three-dimensional points (S6712).The bitstream includes control information and pieces of first attributecontrol information. The control information corresponds to the piecesof attribute information and includes pieces of type information eachindicating a type of different attribute information. The pieces offirst attribute control information correspond one-to-one with thepieces of attribute information. Each of the pieces of first attributecontrol information includes first identification information indicatingthat the first attribute control information is associated with one ofthe pieces of type information.

With such a configuration, since the three-dimensional data decodingdevice can identify the type of the attribute information correspondingto the first attribute control information using the firstidentification information, the three-dimensional data decoding devicecan correctly and efficiently decode attribute information on athree-dimensional point.

For example, the pieces of type information are stored in the controlinformation in a predetermined sequence. The first identificationinformation indicates that first attribute control information includingthe first identification information is associated with one of thepieces of type information that has an order in the predeterminedsequence.

With such a configuration, since type information is indicated in apredetermined sequence without information indicating the typeinformation, the amount of data of the bitstream can be reduced, and theamount of the transmitted bitstream can be reduced.

For example, the bitstream further includes pieces of second attributecontrol information corresponding to the pieces of attributeinformation. Each of the pieces of second attribute control informationincludes a reference value of a parameter used for encoding acorresponding one of the pieces of attribute information.

With such a configuration, since the three-dimensional data decodingdevice can decode the attribute information corresponding to the secondattribute control information using a reference value, thethree-dimensional data decoding device can correctly and efficientlydecode attribute information on a three-dimensional point.

For example, each of the pieces of first attribute control informationincludes difference information that is a difference from the referencevalue of the parameter. With such a configuration, since it is possibleto decode attribute information using a reference value and differenceinformation, it is possible to correctly and efficiently decodeattribute information on a three-dimensional point.

For example, the bitstream further includes pieces of second attributecontrol information corresponding to the pieces of attributeinformation. Each of the pieces of second attribute control informationincludes second identification information indicating that the secondattribute control information is associated with one of the pieces oftype information. With such a configuration, since it is possible toidentify the type of the attribute information corresponding to thesecond attribute control information using the second identificationinformation, it is possible to correctly and efficiently decodeattribute information on a three-dimensional point.

Each of the pieces of first attribute control information includesfields in which parameters are stored. In the decoding, a parameterstored in a specific field among the fields of specific first attributecontrol information among the pieces of first attribute controlinformation is ignored, the specific first attribute control informationcorresponding to a specific type of an attribute.

With such a configuration, since the three-dimensional data decodingdevice can identify the type of the first attribute information usingthe first identification information, the three-dimensional datadecoding device can correctly and efficiently decode attributeinformation on a three-dimensional point.

For example, in the decoding, the pieces of attribute informationencoded are inverse quantized using quantization parameters as theparameters.

With such a configuration, it is possible to correctly decode attributeinformation on a three-dimensional point.

For example, the three-dimensional data decoding device includes aprocessor and memory, and the processor performs the above process usingthe memory.

Embodiment 13

To achieve high compression, attribute information included in PointCloud Compression (PCC) data is transformed in a plurality of methods,such as Lifting, Region Adaptive Hierarchical Transform (RAHT) and othertransformation methods. Here, Lifting is one of transformation methodsusing Level of Detail (LoD).

Important signal information tends to be included in a low frequencycomponent, and therefore the code amount is reduced by quantizing a highfrequency component. That is, the transformation process has strongenergy compression characteristics. In addition, the precision isreduced by the quantization according to the magnitude of thequantization parameter.

FIG. 125 is a block diagram showing a configuration of athree-dimensional data encoding device according to this embodiment. Thethree-dimensional data encoding device includes subtractor 7001,transformer 7002, transformation matrix holder 7003, quantizer 7004,quantization controller 7005, and entropy encoder 7006.

Subtractor 7001 calculates a coefficient value that is the differencebetween input data and reference data. For example, the input data isattribute information included in point cloud data, and the referencedata is a predicted value of the attribute information.

Transformer 7002 performs a transformation process on the coefficientvalue. For example, the transformation process is a process ofclassifying a plurality of pieces of attribute information into LoDs.Note that the transformation process may be Haar transformation or thelike. Transformation matrix holder 7003 holds a transformation matrixused for the transformation process by transformer 7002. For example,the transformation matrix is a Haar transformation matrix. Note thatalthough an example is shown here in which the three-dimensional dataencoding device has both a function of performing a transformationprocess using LoDs and a function of performing a transformation processsuch as Haar transformation, the three-dimensional data encoding devicemay have only any one of the functions. Alternatively, thethree-dimensional data encoding device may selectively use any of thesetwo kinds of transformation processes. Alternatively, thethree-dimensional data encoding device may change the transformationprocess to be used for each predetermined processing unit.

Quantizer 7004 quantizes the coefficient value to generate a quantizedvalue. Quantization controller 7005 controls a quantization parameterused for the quantization by quantizer 7004. For example, quantizationcontroller 7005 may change the quantization parameter (or quantizationstep) according to the hierarchical structure for the encoding. In thisway, an appropriate quantization parameter can be selected for eachlayer of the hierarchical structure, so that the amount of codesoccurring in each layer can be controlled. Quantization controller 7005also sets quantization parameters for a certain layer and the layerslower than the certain layer that include a frequency component that hasa small effect on the subjective image quality at a maximum value, andsets quantization coefficients for the certain layer and the layerslower than the certain layer at 0, for example. In this way, theoccurring code amount can be reduced while reducing the deterioration ofthe subjective image quality. Quantization controller 7005 can alsofinely control the subjective image quality and the occurring codeamount. The “layer” herein refers to a layer (at a depth in a treestructure) in LoD or RAHT (Haar transformation).

Entropy encoder 7006 entropy-encodes (arithmetically encodes, forexample) the quantization coefficient to generate a bitstream. Entropyencoder 7006 also encodes the quantization parameter for each layer setby quantization controller 7005.

FIG. 126 is a block diagram showing a configuration of athree-dimensional data decoding device according to this embodiment. Thethree-dimensional data decoding device includes entropy decoder 7011,inverse quantizer 7012, quantization controller 7013, inversetransformer 7014, transformation matrix holder 7015, and adder 7016.

Entropy decoder 7011 decodes the quantization coefficient and thequantization parameter for each layer from the bitstream. Inversequantizer 7012 inverse-quantizes the quantization coefficient togenerate the coefficient value. Quantization controller 7013 controlsthe quantization parameter used for the inverse quantization by inversequantizer 7012 based on the quantization parameter for each layerobtained in entropy decoder 7011.

Inverse transformer 7014 inverse-transforms the coefficient value. Forexample, inverse transformer 7014 performs inverse Haar transformationon the coefficient value. Transformation matrix holder 7015 holds atransformation matrix used for the inverse transformation process byinverse transformer 7014. For example, the transformation matrix isinverse Haar transformation matrix.

Adder 7016 adds the reference data to the coefficient value to generateoutput data. For example, the output data is attribute informationincluded in point cloud data, and the reference data is a predictedvalue of the attribute information.

Next, the setting of a quantization parameter for each layer will bedescribed. In the encoding of attribute information, such asPredicting/Lifting, a different quantization parameter is used for eachLoD layer. For example, quantization parameters for lower layers are setto be smaller to increase the precision for the lower layers. In thisway, the prediction precision for higher layers can be improved.Quantization parameters for higher layers can be set to be greater,thereby reducing the data amount. In this way, a quantization tree value(Qt) can be separately set for each LoD, according to the use plan ofthe user. Here, the quantization tree value is the quantizationparameter, for example.

FIG. 127 is a diagram showing an example of the setting of LoDs. Asshown in FIG. 127 , for example, independent Qt0 to Qt2 are set for LoD0to LoD2.

In the encoding of the attribute information using RAHT, differentquantization parameters are used according to the depth in the treestructure. For example, quantization parameters for lower layers are setto be smaller to increase the precision for the lower layers. In thisway, the prediction precision for higher layers can be improved.Quantization parameters for higher layers can be set to be greater,thereby reducing the data amount. In this way, a quantization tree value(Qt) can be separately set for each depth in the tree structure,according to the use plan of the user.

FIG. 128 is a diagram showing an example of a hierarchical structure(tree structure) of RAHT. As shown in FIG. 128 , for example,independent Qt0 to Qt2 are set for depths in the tree structure.

In the following, a configuration of a three-dimensional data encodingdevice according to this embodiment will be described. FIG. 129 is ablock diagram showing a configuration of three-dimensional data encodingdevice 7020 according to this embodiment. Three-dimensional dataencoding device 7020 encodes point cloud data (point cloud) to generateencoded data (encoded stream). Three-dimensional data encoding device7020 includes divider 7021, a plurality of geometry information encoders7022, a plurality of attribute information encoders 7023, additionalinformation encoder 7024, and multiplexer 7025.

Divider 7021 generates a plurality of pieces of divisional data bydividing point cloud data. Specifically, divider 7021 generates aplurality of pieces of divisional data by dividing a space of pointcloud data into a plurality of subspaces. Here, a subspace is acombination of tiles or slices or a combination of tiles and slices.More specifically, point cloud data includes geometry information,attribute information (such as color or reflectance), and additionalinformation. Divider 7021 divides geometry information into a pluralityof pieces of divisional geometry information, and divides attributeinformation into a plurality of pieces of divisional attributeinformation. Divider 7021 also generates additional informationconcerning the division.

Divider 7021 first divides a point cloud into tiles, for example.Divider 7021 then further divides the resulting tiles into slices.

The plurality of geometry information encoders 7022 generate a pluralityof pieces of encoded geometry information by encoding a plurality ofpieces of divisional geometry information. For example, geometryinformation encoders 7022 encode divisional geometry information usingan N-ary tree structure, such as an octree. Specifically, in the case ofan octree, a current space is divided into eight nodes (subspaces), and8-bit information (occupancy code) that indicates whether each nodeincludes a point cloud or not is generated. A node including a pointcloud is further divided into eight nodes, and 8-bit information thatindicates whether each of the eight nodes includes a point cloud or notis generated. This process is repeated until a predetermined layer isreached or the number of the point clouds included in each node becomesequal to or less than a threshold. For example, the plurality ofgeometry information encoders 7022 process a plurality of pieces ofdivisional geometry information in parallel.

Attribute information encoder 7023 generates encoded attributeinformation, which is encoded data, by encoding attribute informationusing configuration information generated by geometry informationencoder 7022. For example, attribute information encoder 7023 determinesa reference point (reference node) that is to be referred to in encodinga current point (current node) to be processed based on the octreestructure generated by geometry information encoder 7022. For example,attribute information encoder 7023 refers to a node whose parent node inthe octree is the same as the parent node of the current node, ofperipheral nodes or neighboring nodes. Note that the method ofdetermining a reference relationship is not limited to this method.

The process of encoding geometry information or attribute informationmay include at least one of a quantization process, a predictionprocess, and an arithmetic encoding process. In this case, “refer to”means using a reference node for calculation of a predicted value ofattribute information or using a state of a reference node (occupancyinformation that indicates whether a reference node includes a pointcloud or not, for example) for determination of a parameter of encoding.For example, the parameter of encoding is a quantization parameter inthe quantization process or a context or the like in the arithmeticencoding.

Additional information encoder 7024 generates encoded additionalinformation by encoding the additional information included in the pointcloud data and the additional information concerning the data divisiongenerated in the division by divider 7021.

Multiplexer 7025 generates encoded stream (encoded stream) bymultiplexing the plurality of pieces of encoded geometry information,the plurality of pieces of encoded attribute information, and theencoded additional information, and transmits the generated encodeddata. The encoded additional information is used in the decoding.

FIG. 130 is a block diagram of divider 7021. Divider 7021 includes tiledivider 7031 and slice divider 7032.

Tile divider 7031 generates a plurality of pieces of tile geometryinformation by dividing geometry information (position (geometry)) intotiles. Tile divider 7031 generates a plurality of pieces of tileattribute information by dividing attribute information (attribute) intotiles. Tile divider 7031 also outputs tile additional information (TileMetaData) including information concerning the tile division andinformation generated in the tile division.

Slice divider 7032 generates a plurality of pieces of divisionalgeometry information (a plurality of pieces of slice geometryinformation) by dividing a plurality of pieces of tile geometryinformation into slices. Slice divider 7032 generates a plurality ofpieces of divisional attribute information (a plurality of pieces ofslice attribute information) by dividing a plurality of pieces of tileattribute information into slices. Slice divider 7032 also outputs sliceadditional information (Slice MetaData) including information concerningthe slice division and information generated in the slice division.

Tile divider 7031 and slice divider 7032 also determine a quantizationtree value (quantization parameter) based on the generated additionalinformation.

FIG. 131 is a block diagram of attribute information encoder 7023.Attribute information encoder 7023 includes transformer 7035, quantizer7036, and entropy encoder 7037.

Transformer 7035 classifies the divisional attribute information intolayers, such as LoDs, and generates a coefficient value (differencevalue) by calculating the difference between the divisional attributeinformation and the predicted value. Note that transformer 7035 maygenerate the coefficient value by performing the Haar transformation onthe divisional attribute information.

Quantizer 7036 generates a quantized value by quantizing the coefficientvalue. Specifically, quantizer 7036 divides the coefficient by aquantization step based on the quantization parameter. Entropy encoder7037 generates encoded attribute information by entropy-encoding thequantized value.

In the following, a configuration of a three-dimensional data decodingdevice according to this embodiment will be described. FIG. 132 is ablock diagram showing a configuration of three-dimensional data decodingdevice 7040. Three-dimensional data decoding device 7040 reproducespoint cloud data by decoding encoded data (encoded stream) generated byencoding the point cloud data. Three-dimensional data decoding device7040 includes demultiplexer 7041, a plurality of geometry informationdecoders 7042, a plurality of attribute information decoders 7043,additional information decoder 7044, and combiner 7045.

Demultiplexer 7041 generates a plurality of pieces of encoded geometryinformation, a plurality of pieces of encoded attribute information, andencoded additional information by demultiplexing encoded data (encodedstream).

The plurality of geometry information decoders 7042 generates aplurality of pieces of divisional geometry information by decoding aplurality of pieces of encoded geometry information. For example, theplurality of geometry information decoders 7042 process a plurality ofpieces of encoded geometry information in parallel.

The plurality of attribute information decoders 7043 generate aplurality of pieces of divisional attribute information by decoding aplurality of pieces of encoded attribute information. For example, theplurality of attribute information decoders 7043 process a plurality ofpieces of encoded attribute information in parallel.

A plurality of additional information decoders 7044 generate additionalinformation by decoding encoded additional information.

Combiner 7045 generates geometry information by combining a plurality ofpieces of divisional geometry information using additional information.Combiner 7045 generates attribute information by combining a pluralityof pieces of divisional attribute information using additionalinformation.

FIG. 133 is a block diagram of attribute information decoder 7043.Attribute information decoder 7043 includes entropy decoder 7051,inverse quantizer 7052, and inverse transformer 7053. Entropy decoder7051 generates a quantized value by entropy-decoding encoded attributeinformation. Inverse quantizer 7052 generates a coefficient value byinverse-quantizing the quantized value. Specifically, inverse quantizer7052 multiplies the coefficient value by a quantization step based onthe quantization tree value (quantization parameter) obtained from thebitstream. Inverse transformer 7053 generates divisional attributeinformation by inverse-transforming the coefficient value. Here, theinverse transformation is a process of adding the predicted value to thecoefficient value, for example. Alternatively, the inversetransformation is the inverse Haar transformation.

In the following, an example of a method of determining a quantizationparameter will be described. FIG. 134 is a diagram showing an example ofthe setting of a quantization parameter in the tile division and theslice division.

When the value of the quantization parameter is small, the originalinformation is likely to be maintained. For example, a default value ofthe quantization parameter is 1. For example, in the encoding processusing tiles of PCC data, a quantization parameter for a tile of a mainroad is set to be a small value, in order to maintain the data quality.On the other hand, a quantization parameter for a tile of a peripheralarea is set to be a great value. In this way, the coding efficiency canbe improved, while the data quality of the peripheral area decreases.

Similarly, in the encoding process using slices of PCC data, a sidewalk,a tree, and a building are important in the self-position estimation andmapping, and a quantization parameter for a slice of a sidewalk, a tree,or a building is set to be a small value. On the other hand, a movingbody or other objects is less important, so that a quantizationparameter for a slice of a moving body or other objects is set to be agreat value.

When ΔQP (DeltaQP) described later is used, in the encoding of athree-dimensional point belonging to an important area, such as a mainroad, the three-dimensional data encoding device may perform theencoding by setting the value of ΔQP to be a negative value to reducethe quantization error, in order to decrease the quantization parameter.In this way, the decoded attribute value of the three-dimensional pointbelonging to the important area can be brought close to the value beforethe encoding. In the encoding of a three-dimensional point belonging toan area that is not important, such as a peripheral area, thethree-dimensional data encoding device may set the value of ΔQP to be apositive value to reduce the information amount, in order to increasethe quantization parameter. In this way, the total code amount can bereduced, while maintaining the amount of information on the importantarea.

In the following, an example of information that indicates aquantization parameter for each layer will be described. In encodingattribute information on a three-dimensional point by quantization, ascheme for controlling quantization parameters on a finer unit basis isintroduced in addition to quantization parameter QPbase for a frame, aslice, a tile, or the like. For example, when encoding attributeinformation using LoDs, the three-dimensional data encoding deviceperform the encoding by changing the value of the quantization parameterfor each LoD by providing Delta_Layer for each LoD and addingDelta_Layer to the value of QPbase for each LoD. The three-dimensionaldata encoding device also adds Delta_Layer used for the encoding to aheader or the like of the bitstream. In this way, the three-dimensionaldata encoding device can encode attribute information on athree-dimensional point by changing the quantization parameter for eachLoD according to a desired code amount and an actual code amount, forexample, and therefore can finally generate a bitstream having a codeamount close to the desired code amount. The three-dimensional datadecoding device can properly decode the bitstream by decoding QPbase andDelta_Layer included in the header to generate the quantizationparameters used by the three-dimensional data encoding device.

FIG. 135 is a diagram showing an example in which attribute informationon all three-dimensional points are encoded using quantization parameterQPbase. FIG. 136 is a diagram showing an example in which encoding isperformed by changing the quantization parameter for each LoD layer. Inthe example shown in FIG. 136 , the quantization parameter for theleading LoD is calculated by adding Delta_Layer of the leading LoD toQPbase. For the second and following LoDs, the quantization parameterfor the LoD being processed is calculated by adding Delta_Layer of theLoD being processed to the quantization parameter for the immediatelypreceding LoD. For example, quantization parameter QP3 of at the head ofLoD3 is calculated according to QP3=QP2+Delta_Layer[3].

Note that Delta_Layer[i] for each LoD may indicate the difference valuewith respect to QPbase. That is, quantization parameter QPi of i-th LoDiis indicated by QPi=QPbase+Delta_Layer[i]. For example,QP1=QPbase+Delta_Layer[1], and QP2=QPbase+Delta_Layer[2].

FIG. 137 is a diagram showing a syntax example of an attributeinformation header (attribute header information). Here, the attributeinformation header is a header on a frame, slice or tile basis, forexample, and is a header of attribute information. As shown in FIG. 137, the attribute information header includes QPbase (referencequantization parameter), NumLayer (number of layers), and Delta_Layer[i](differential quantization parameter).

QPbase indicates the value of a reference quantization parameter for aframe, a slice, a tile, or the like. NumLayer indicates the number oflayers of LoD or RAHT. In other words, NumLayer indicates the number ofall Delta_Layer[i] included in the attribute information header.

Delta_Layer[i] indicates the value of ΔQP for layer i. Here, ΔQP is avalue obtained by subtracting the quantization parameter for layer ifrom the quantization parameter for layer i−1. Note that ΔQP may be avalue obtained by subtracting the quantization parameter for layer ifrom QPbase. ΔQP can assume a positive or negative value. Note thatDelta_Layer[0] need not be added to the header. In that case, thequantization parameter for layer 0 is equal to QPbase. In this way, thecode amount of the header can be reduced.

FIG. 138 is a diagram showing another syntax example of an attributeinformation header (attribute header information). The attributeinformation header shown in FIG. 138 differs from the attributeinformation header shown in FIG. 137 in that the attribute informationheader further includes delta_Layer_present_flag.

delta_Layer_present_flag is a flag that indicates whether Delta_Layer isincluded in the bitstream or not. For example, a value of 1 indicatesthat Delta_Layer is included in the bitstream, and a value of 0indicates that Delta_Layer is not included in the bitstream. Whendelta_Layer_present_flag is 0, the three-dimensional data decodingdevice performs the following decoding process by setting Delta_Layer tobe 0, for example.

Note that although examples have been described here in which thequantization parameter is indicated by QPbase and Delta_Layer, aquantization step may be indicated by QPbase and Delta_Layer. Thequantization step is calculated from the quantization parameter using aformula, a table or the like determined in advance. In the quantizationprocess, the three-dimensional data encoding device divides thecoefficient value by the quantization step. In the inverse quantizationprocess, the three-dimensional data decoding device reproduces thecoefficient value by multiplying the quantized value by the quantizationstep.

Next, an example in which the quantization parameters are controlled ona finer unit basis will be described. FIG. 139 is a diagram showing anexample in which the quantization parameters are controlled on a basisof a unit finer than LoD.

For example, when encoding attribute information using LoD, thethree-dimensional data encoding device defines ADelta_QP andNumPointADelta, which represents the geometry information on athree-dimensional point to which ADelta_QP is to be added, in additionto Delta_Layer for each LoD layer. The three-dimensional data encodingdevice performs the encoding by changing the value of the quantizationparameter based on Delta_Layer, ADelta_QP, and NumPointADelta.

The three-dimensional data encoding device may add ADelta andNumPointADelta used for the encoding to the header or the like of thebitstream. This allows the three-dimensional data encoding device toencode attribute information on a plurality of three-dimensional pointsby changing the quantization parameter for each three-dimensional pointaccording to the desired code amount and the actual code amount, forexample. In this way, the three-dimensional data encoding device canfinally generate a bitstream having a code amount close to the desiredcode amount. The three-dimensional data decoding device can properlydecode the bitstream by decoding QPbase, Delta_Layer, and ADeltaincluded in the header to generate the quantization parameters used bythe three-dimensional data encoding device.

For example, as shown in FIG. 139 , quantized value QP4 of N0-thattribute information is calculated according to QP4=QP3+ADelta_QP[0].

An encoding/decoding order reverse to the encoding/decoding order shownin FIG. 139 can also be used. For example, encoding/decoding can also beperformed in the order of LoD3, LoD2, LoD1, and then LoD0.

FIG. 140 is a diagram showing a syntax example of an attributeinformation header (attribute header information) in the case where theexample shown in FIG. 139 is used. The attribute information headershown in FIG. 140 differs from the attribute information header shown inFIG. 137 in that the attribute information header further includesNumADelta, NumPointADelta[i], and ADelta_QP[i].

NumADelta indicates the number of all ADelta_QP included in thebitstream. NumPointADelta[i] indicates an identification number ofthree-dimensional point A to which ADelta_QP[i] is applied. For example,NumPointADelta[i] indicates the number of the three-dimensional pointsfrom the leading three-dimensional point to three-dimensional point A inthe encoding/decoding order. NumPointADelta[i] may also indicates thenumber of the three-dimensional points from the first three-dimensionalpoint to three-dimensional point A in the LoD to which three-dimensionalpoint A belongs.

Alternatively, NumPointADelta[i] may indicate the difference valuebetween the identification number of the three-dimensional pointindicated by NumPointADelta[i−1] and the identification number ofthree-dimensional point A. In this way, the value of NumPointADelta[i]can be reduced, so that the code amount can be reduced.

ADelta_QP[i] indicates the value of ΔQP of the three-dimensional pointindicated by NumPointADelta[i]. That is, ADelta_QP[i] indicates thedifference between the quantization parameter of the three-dimensionalpoint indicated by NumPointADelta[i] and the quantization parameter ofthe three-dimensional point immediately preceding that three-dimensionalpoint.

FIG. 141 is a diagram showing another syntax example of an attributeinformation header (attribute header information) in the case where theexample shown in FIG. 139 is used. The attribute information headershown in FIG. 141 differs from the attribute information header shown inFIG. 140 in that the attribute information header further includesdelta_Layer_present_flag and additional_delta_QP_present_flag andincludes NumADelta_minus1 instead of NumADelta.

delta_Layer_present_flag is the same as that already described withreference to FIG. 138 .

additional_delta_QP_present_flag is a flag that indicates whetherADelta_QP is included in the bitstream or not. For example, a value of 1indicates that ADelta_QP is included in the bitstream, and a value of 0indicates that ADelta_QP is not included in the bitstream. Whenadditional_delta_QP_present_flag is 0, the three-dimensional datadecoding device performs the following decoding process by settingADelta_QP to be 0, for example.

NumADelta_minus1 indicates the number of all ADelta_QP included in thebitstream minus1. In this way, by adding the value obtained bysubtracting 1 from the number of ADelta_QP to the header, the codeamount of the header can be reduced. For example, the three-dimensionaldata decoding device calculates NumADelta=NumADelta_minus1+1.ADelta_QP[i] indicates the value of i-th ADelta_QP. Note thatADelta_QP[i] can be set to be not only a positive value but also anegative value.

FIG. 142 is a flowchart of a three-dimensional data encoding processaccording to this embodiment. First, the three-dimensional data encodingdevice encodes geometry information (geometry) (S7001). For example, thethree-dimensional data encoding device performs the encoding using anoctree representation.

The three-dimensional data encoding device then transforms attributeinformation (S7002). For example, after the encoding of the geometryinformation, if the position of a three-dimensional point is changedbecause of quantization or the like, the three-dimensional data encodingdevice reassigns the attribute information on the originalthree-dimensional point to the three-dimensional point changed inposition. Note that the three-dimensional data encoding device mayperform the reassignment by interpolation of values of the attributeinformation according to the amount of change in position. For example,the three-dimensional data encoding device detects N three-dimensionalpoints yet to be changed in position close to the three-dimensionalposition of the three-dimensional point changed in position, takes aweighted average of the values of the attribute information on the Nthree-dimensional points based on the distance between thethree-dimensional positions of the three-dimensional point changed inposition and each of the N three-dimensional points, and determines theresulting value as the value of the attribute information on thethree-dimensional point changed in position. If the three-dimensionalpositions of two or more three-dimensional points are changed to thesame three-dimensional position because of quantization or the like, thethree-dimensional data encoding device may assign an average value ofthe attribute information on the two or more three-dimensional pointsyet to be changed in position as the value of the attribute informationon the three-dimensional points changed in position.

The three-dimensional data encoding device then encodes the attributeinformation (S7003). When the three-dimensional data encoding deviceencodes a plurality of pieces of attribute information, for example, thethree-dimensional data encoding device may sequentially encode theplurality of pieces of attribute information. For example, when thethree-dimensional data encoding device encodes color and reflectance asattribute information, the three-dimensional data encoding devicegenerates a bitstream including the result of encoding of color followedby the result of encoding of reflectance. Note that the plurality ofresults of encoding of attribute information can be included in thebitstream in any order.

The three-dimensional data encoding device may add informationindicating a starting point of the encoded data of each attributeinformation in the bitstream to the header or the like. In this way, thethree-dimensional data decoding device can selectively decode attributeinformation that needs to be decoded, and therefore can omit thedecoding process for attribute information that does not need to bedecoded. Therefore, the processing amount of the three-dimensional datadecoding device can be reduced. The three-dimensional data encodingdevice may encode a plurality of pieces of attribute information inparallel, and integrate the results of the encoding into one bitstream.In this way, the three-dimensional data encoding device can encode aplurality of pieces of attribute information at a high speed.

FIG. 143 is a flowchart of the attribute information encoding process(S7003). First, the three-dimensional data encoding device sets an LoD(S7011). That is, the three-dimensional data encoding device assignseach three-dimensional point to any of a plurality of LoDs.

The three-dimensional data encoding device then starts a loop on an LoDbasis (S7012). That is, the three-dimensional data encoding devicerepeatedly performs the process from step S7013 to step S7021 for eachLoD.

The three-dimensional data encoding device then starts a loop on a basisof a three-dimensional point (S7013). That is, the three-dimensionaldata encoding device repeatedly performs the process from step S7014 tostep S7020 for each three-dimensional point.

First, the three-dimensional data encoding device searches for aplurality of peripheral points, which are three-dimensional pointspresent in the periphery of the current three-dimensional point, thatare to be used for calculation of a predicted value of the currentthree-dimensional point to be processed (S7014). The three-dimensionaldata encoding device then calculates a weighted average of values of theattribute information on the plurality of peripheral points, and setsthe obtained value as predicted value P (S7015). The three-dimensionaldata encoding device then calculates a prediction residual, which is thedifference between the attribute information and the predicted value ofthe current three-dimensional point (S7016). The three-dimensional dataencoding device then calculates a quantized value by quantizing theprediction residual (S7017). The three-dimensional data encoding devicethen arithmetically encodes the quantized value (S7018). Thethree-dimensional data encoding device then determines ΔQP (S7019). ΔQPdetermined here is used for determining a quantization parameter usedfor quantization of a subsequent prediction residual.

The three-dimensional data encoding device then calculates aninverse-quantized value by inverse-quantizing the quantized value(S7020). The three-dimensional data encoding device then generates adecoded value by adding the predicted value to the inverse-quantizedvalue (S7021). The three-dimensional data encoding device then ends theloop on a basis of a three-dimensional point (S7022). Thethree-dimensional data encoding device also ends the loop on a LoD basis(S7023).

FIG. 144 is a flowchart of the ΔQP determination process (S7019). First,the three-dimensional data encoding device calculates layer i to whichcurrent three-dimensional point A to be encoded next belongs andencoding order N (S7031). Layer i indicates a LoD layer or a RAHT layer,for example.

The three-dimensional data encoding device then adds the actual codeamount to a cumulative code amount (S7032). Here, the cumulative codeamount refers to a cumulative code amount for one frame, one slice, orone tile of the current three-dimensional point. Note that thecumulative code amount may refer to a cumulative code amount for aplurality of frames, a plurality of slices, or a plurality of tiles.Alternatively, a cumulative code amount of attribute information may beused, or a cumulative code amount of both geometry information andattribute information may be used.

The three-dimensional data encoding device then determines whether thecumulative code amount is greater than the desired code amount×TH1 ornot (S7033). Here, the desired code amount refers to a desired codeamount for one frame, one slice, or one tile of the currentthree-dimensional point. Note that the desired code amount may refer toa desired code amount for a plurality of frames, a plurality of slices,or a plurality of tiles. Alternatively, a desired code amount ofattribute information may be used, or a desired code amount of bothgeometry information and attribute information may be used.

When the cumulative code amount is equal to or smaller than the desiredcode amount×TH1 (if No in S7033), the three-dimensional data encodingdevice determines whether the cumulative code amount is greater than thedesired code amount×TH2 or not (S7036).

Here, as thresholds TH1 and TH2, values from 0.0 to 1.0 are set, forexample. In addition, TH1>TH2. For example, when the cumulative codeamount is greater than the value of the desired code amount×TH1 (if Yesin S7033), the three-dimensional data encoding device determines thatthe code amount needs to be reduced as early as possible, and setsADelta_QP to value a in order to increase the quantization parameter fornext three-dimensional point N. The three-dimensional data encodingdevice also sets NumPointADelta to value N, and increment j by 1(S7034). The three-dimensional data encoding device then addsADelta_QP=α and NumPointADelta=N to the header (S7035). Note that valueα may be a fixed value or a variable value. For example, thethree-dimensional data encoding device may determine value α based onthe magnitude of the difference between the cumulative code amount andthe desired code amount×TH1. For example, the three-dimensional dataencoding device sets value α to be greater as the difference between thecumulative code amount and the desired code amount×TH1 increases. Inthis way, the three-dimensional data encoding device can control thequantization parameter so that the cumulative code amount does notexceed the desired code amount.

When the cumulative code amount is greater than the desired codeamount×TH2 (if Yes in S7036), the three-dimensional data encoding devicesets Delta_Layer to value β in order to increase the quantizationparameter for layer i to which current three-dimensional point A belongsor the subsequent layer i+1 (S7037). For example, the three-dimensionaldata encoding device sets Delta_Layer[i] of layer i to be value β whencurrent three-dimensional point A is at the top of layer i, and setsDelta_Layer[i+1] of layer i+1 to be value β when currentthree-dimensional point A is not at the top of layer i.

The three-dimensional data encoding device adds Delta_Layer=β of layer ior layer i+1 to the header (S7038). Note that value β may be a fixedvalue or a variable value. For example, the three-dimensional dataencoding device may determine value β based on the magnitude of thedifference between the cumulative code amount and the desired codeamount×TH2. For example, the three-dimensional data encoding device setsvalue β to be greater as the difference between the cumulative codeamount and the desired code amount×TH2 increases. In this way, thethree-dimensional data encoding device can control the quantizationparameter so that the cumulative code amount does not exceed the desiredcode amount.

If the cumulative code amount exceeds or is about to exceed the desiredcode amount, the three-dimensional data encoding device may set thevalue of ADelta_QP or Delta_Layer so that the quantization parameterassumes the maximum value supported by the standard or the like. In thisway, the three-dimensional data encoding device can set the quantizationcoefficient for points subsequent to three-dimensional point A or layerssubsequent to layer i to be 0, thereby reducing the increase of theactual code amount and preventing the cumulative code amount fromexceeding the desired code amount.

If the cumulative code amount is smaller than the desired codeamount×TH3, the three-dimensional data encoding device may decrease thequantization parameter so that the actual code amount increases. Forexample, the three-dimensional data encoding device may decrease thequantization parameter by setting the value of Delta_Layer or ADelta_QPto be a negative value depending on the difference between thecumulative code amount and the desired code amount. In this way, thethree-dimensional data encoding device can generate a bitstream having acode amount close to the desired code amount.

FIG. 145 is a flowchart of a three-dimensional data decoding processaccording to this embodiment. First, the three-dimensional data decodingdevice decodes geometry information (geometry) from the bitstream(S7005). For example, the three-dimensional data decoding deviceperforms the decoding using an octree representation.

The three-dimensional data decoding device then decodes attributeinformation from the bitstream (S7006). For example, when thethree-dimensional data decoding device decodes a plurality of pieces ofattribute information, the three-dimensional data decoding device maysequentially decode the plurality of pieces of attribute information.For example, when the three-dimensional data decoding device decodescolor and reflectance as attribute information, the three-dimensionaldata decoding device may decode the result of encoding of color and theresult of encoding of reflectance in the order thereof in the bitstream.For example, if the result of encoding of color is followed by theresult of encoding of reflectance in the bitstream, thethree-dimensional data decoding device first decodes the result ofencoding of color and then decodes the result of encoding ofreflectance. Note that the three-dimensional data decoding device candecode the result of encoding of attribute information in the bitstreamin any order.

The three-dimensional data decoding device may obtain the informationindicating the starting point of the encoded data of each piece ofattribute information in the bitstream by decoding the header or thelike. In this way, the three-dimensional data decoding device canselectively decode attribute information that needs to be decoded, andtherefore can omit the decoding process for attribute information thatdoes not need to be decoded. Therefore, the processing amount of thethree-dimensional data decoding device can be reduced. Thethree-dimensional data decoding device may decode a plurality of piecesof attribute information in parallel, and integrate the results of thedecoding into one three-dimensional point cloud. In this way, thethree-dimensional data decoding device can decode a plurality of piecesof attribute information at a high speed.

FIG. 146 is a flowchart of the attribute information decoding process(S7006). First, the three-dimensional data decoding device sets an LoD(S7041). That is, the three-dimensional data decoding device assignseach of a plurality of three-dimensional points having decoded geometryinformation to any of a plurality of LoDs. For example, the method ofthe assignment is the same as the method of assignment used in thethree-dimensional data encoding device.

The three-dimensional data decoding device then decodes ΔQP from thebitstream (S7042). Specifically, the three-dimensional data decodingdevice decodes Delta_Layer, ADelta_QP, and NumPointADelta from theheader of the bitstream.

The three-dimensional data decoding device then starts a loop on an LoDbasis (S7043). That is, the three-dimensional data decoding devicerepeatedly performs the process from step S7044 to step S7050 for eachLoD.

The three-dimensional data decoding device then starts a loop on a basisof a three-dimensional point (S7044). That is, the three-dimensionaldata decoding device repeatedly performs the process from step S7045 tostep S7049 for each three-dimensional point.

First, the three-dimensional data decoding device searches for aplurality of peripheral points, which are three-dimensional pointspresent in the periphery of the current three-dimensional point, thatare to be used for calculation of a predicted value of the currentthree-dimensional point to be processed (S7045). The three-dimensionaldata decoding device then calculates a weighted average of values of theattribute information on the plurality of peripheral points, and setsthe obtained value as predicted value P (S7046). Note that theseprocessings are the same as those in the three-dimensional data encodingdevice.

The three-dimensional data decoding device then arithmetically decodesthe quantized value from the bitstream (S7047). The three-dimensionaldata decoding device then calculates an inverse-quantized value byinverse-quantizing the decoded quantized value (S7048). In this inversequantization, a quantization parameter calculated using ΔQP obtained instep S7042 is used.

The three-dimensional data decoding device then generates a decodedvalue by adding the predicted value to the inverse-quantized value(S7049). The three-dimensional data decoding device then ends the loopon a basis of a three-dimensional point (S7050). The three-dimensionaldata decoding device also ends the loop on a LoD basis (S7051).

FIG. 147 is a block diagram of attribute information encoder 7023.Attribute information encoder 7023 includes LoD setter 7061, searcher7062, predictor 7063, subtractor 7064, quantizer 7065, inverse quantizer7066, reconstructor 7067, memory 7068, and ΔQP calculator 7070.

LoD setter 7061 generates a LoD using geometry information on athree-dimensional point. Searcher 7062 searches for a neighboringthree-dimensional point of each three-dimensional point using a LoDgeneration result and distance information between three-dimensionalpoints. Predictor 7063 generates a predicted value of attributeinformation of a current three-dimensional point. Predictor 7063 alsoassigns a predicted value to a plurality of prediction modes 0 to M−1,and selects a prediction mode to be used from the plurality ofprediction modes.

Subtractor 7064 generates a prediction residual by subtracting thepredicted value from the attribute information. Quantizer 7065 quantizesthe prediction residual of the attribute information. Inverse quantizer7066 inverse-quantizes the quantized prediction residual. Reconstructor7067 generates a decoded value by summing the predicted value and theinverse-quantized prediction residual. Memory 7068 stores the value(decoded value) of the decoded attribute information on eachthree-dimensional point. The decoded attribute information on thethree-dimensional points stored in memory 7068 is used for prediction ofa three-dimensional point yet to be encoded by predictor 7063.

Arithmetic encoder 7069 calculates ZeroCnt from the quantized predictionresidual, and arithmetically encodes ZeroCnt. Arithmetic encoder 7069also arithmetically encodes any quantized prediction residual that isnot zero. Arithmetic encoder 7069 may binarize the prediction residualbefore the arithmetic encoding. Arithmetic encoder 7069 may generate andencode various kinds of head information. Arithmetic encoder 7069 mayarithmetically encode prediction mode information (PredMode) thatindicates the prediction mode used for the encoding by predictor 7063,and add the information to the bitstream.

ΔQP calculator 7070 determines values of Delta_Layer, ADelta_QP, andNumPointADelta from the actual code amount obtained by arithmeticencoder 7069 and the predetermined desired code amount. The quantizationby quantizer 7065 is performed using a quantization parameter based onthe determined Delta_Layer, ADelta_QP, and NumPointADelta. Arithmeticencoder 7069 arithmetically encodes Delta_Layer, ADelta_QP, andNumPointADelta and adds these values to the bitstream.

FIG. 148 is a block diagram of attribute information decoder 7043.Attribute information decoder 7043 includes arithmetic decoder 7071, LoDsetter 7072, searcher 7073, predictor 7074, inverse quantizer 7075,reconstructor 7076, and memory 7077.

Arithmetic decoder 7071 arithmetically decodes ZeroCnt and theprediction residual included in the bitstream. Arithmetic decoder 7071also decodes various kinds of header information. Arithmetic decoder7071 also arithmetically decodes prediction mode information (PredMode)from the bitstream, and outputs the obtained prediction mode informationto predictor 7074. Arithmetic decoder 7071 also decodes Delta_Layer,ADelta_QP, and NumPointADelta from the header of the bitstream.

LoD setter 7072 generates a LoD using decoded geometry information on athree-dimensional point. Searcher 7073 searches for a neighboringthree-dimensional point of each three-dimensional point using a LoDgeneration result and distance information between three-dimensionalpoints.

Predictor 7074 generates a predicted value of attribute information of acurrent three-dimensional point to be decoded. Inverse quantizer 7075inverse-quantizes the arithmetically decoded prediction residual.Specifically, inverse quantizer 7075 performs inverse quantization usinga quantization parameter based on the decoded Delta_Layer, ADelta_QP,and NumPointADelta.

Reconstructor 7076 generates a decoded value by summing the predictedvalue and the inverse-quantized prediction residual. Memory 7077 storesthe value (decoded value) of the decoded attribute information on eachthree-dimensional point. The decoded attribute information on thethree-dimensional points stored in memory 7077 is used for prediction ofa three-dimensional point yet to be decoded by predictor 7074.

In the following, an example in which RAHT layers are used instead ofthe LoD layers will be described. FIG. 149 is a diagram showing anexample in which the quantization parameters are controlled on a basisof a finer unit when attribute information is encoded using RAHT. Forexample, when encoding attribute information using RAHT, thethree-dimensional data encoding device defines ADelta_QP andNumPointADelta, which represents the geometry information on athree-dimensional point to which ADelta_QP is to be added, in additionto Delta_Layer for each RAHT layer. The three-dimensional data encodingdevice performs the encoding by changing the value of the quantizationparameter based on Delta_Layer, ADelta_QP, and NumPointADelta.

The three-dimensional data encoding device may add ADelta andNumPointADelta used for the encoding to the header or the like of thebitstream. This allows the three-dimensional data encoding device toencode attribute information on three-dimensional points by changing thequantization parameter for each three-dimensional point according to thedesired code amount and the actual code amount, for example. In thisway, the three-dimensional data encoding device can finally generate abitstream having a code amount close to the desired code amount. Thethree-dimensional data decoding device can properly decode the bitstreamby decoding QPbase, Delta_Layer, and ADelta included in the header togenerate the quantization parameters used by the three-dimensional dataencoding device.

For example, quantized value QP4 of N0-th attribute information iscalculated according to QP4=QP3+ADelta_QP[0]. Each ADelta_QP[i] may bethe difference value with respect to QPbase, likeQP4=QPbase+ADelta_QP[0].

FIG. 150 is a diagram showing a syntax example of an attributeinformation header (attribute header information) in the case where theexample shown in FIG. 149 is used. The attribute information headershown in FIG. 150 is basically the same as the attribute informationheader shown in FIG. 140 but differs in that RAHT layers are usedinstead of LoD layers.

NumADelta indicates the number of all ADelta_QP included in thebitstream. NumPointADelta[i] indicates an identification number ofthree-dimensional point A to which ADelta_QP[i] is applied. For example,NumPointADelta[i] indicates the number of the three-dimensional pointsfrom the leading three-dimensional point to three-dimensional point A inthe encoding/decoding order. NumPointADelta[i] may also indicates thenumber of the three-dimensional points from the first three-dimensionalpoint to three-dimensional point A in the layer to whichthree-dimensional point A belongs.

Alternatively, NumPointADelta[i] may indicate the difference valuebetween the identification number of the three-dimensional pointindicated by NumPointADelta[i−1] and the identification number ofthree-dimensional point A. In this way, the value of NumPointADelta[i]can be reduced, so that the code amount can be reduced.

FIG. 151 is a diagram showing another syntax example of an attributeinformation header (attribute header information) in the case where theexample shown in FIG. 149 is used. Note that the attribute informationheader shown in FIG. 151 is basically the same as the attributeinformation header shown in FIG. 141 but differs in that RAHT layers areused instead of LoD layers.

additional_delta_QP_present_flag is a flag that indicates whetherADelta_QP is included in the bitstream or not. For example, a value of 1indicates that ADelta_QP is included in the bitstream, and a value of 0indicates that ADelta_QP is not included in the bitstream. Whenadditional_delta_QP_present_flag is 0, the three-dimensional datadecoding device performs the following decoding process by settingADelta_QP to be 0, for example.

NumADelta_minus1 indicates the number of all ADelta_QP included in thebitstream minus1. In this way, by adding the value obtained bysubtracting 1 from the number of ADelta_QP to the header, the codeamount of the header can be reduced. For example, the three-dimensionaldata decoding device calculates NumADelta=NumADelta_minus1+1.ADelta_QP[i] indicates the value of i-th ADelta_QP. Note thatADelta_QP[i] can be set to be not only a positive value but also anegative value.

FIG. 152 is a flowchart of a three-dimensional data encoding process inthe case where RAHT is used. First, the three-dimensional data encodingdevice encodes geometry information (geometry) (S7061). For example, thethree-dimensional data encoding device performs the encoding using anoctree representation.

The three-dimensional data encoding device then transforms attributeinformation (S7062). For example, after the encoding of the geometryinformation, if the position of a three-dimensional point is changedbecause of quantization or the like, the three-dimensional data encodingdevice reassigns the attribute information on the originalthree-dimensional point to the three-dimensional point changed inposition. Note that the three-dimensional data encoding device mayperform the reassignment by interpolation of values of the attributeinformation according to the amount of change in position. For example,the three-dimensional data encoding device detects N three-dimensionalpoints yet to be changed in position close to the three-dimensionalposition of the three-dimensional point changed in position, takes aweighted average of the values of the attribute information on the Nthree-dimensional points based on the distance between thethree-dimensional positions of the three-dimensional point changed inposition and each of the N three-dimensional points, and determines theresulting value as the value of the attribute information on thethree-dimensional point changed in position. If the three-dimensionalpositions of two or more three-dimensional points are changed to thesame three-dimensional position because of quantization or the like, thethree-dimensional data encoding device may assign an average value ofthe attribute information on the two or more three-dimensional pointsyet to be changed in position as the value of the attribute informationon the three-dimensional points changed in position.

The three-dimensional data encoding device then encodes the attributeinformation (S7063). When the three-dimensional data encoding deviceencodes a plurality of pieces of attribute information, for example, thethree-dimensional data encoding device may sequentially encode theplurality of pieces of attribute information. For example, when thethree-dimensional data encoding device encodes color and reflectance asattribute information, the three-dimensional data encoding devicegenerates a bitstream including the result of encoding of color followedby the result of encoding of reflectance. Note that the plurality ofresults of encoding of attribute information can be included in thebitstream in any order.

The three-dimensional data encoding device may add informationindicating a starting point of the encoded data of each attributeinformation in the bitstream to the header or the like. In this way, thethree-dimensional data decoding device can selectively decode attributeinformation that needs to be decoded, and therefore can omit thedecoding process for attribute information that does not need to bedecoded. Therefore, the processing amount of the three-dimensional datadecoding device can be reduced. The three-dimensional data encodingdevice may encode a plurality of pieces of attribute information inparallel, and integrate the results of the encoding into one bitstream.In this way, the three-dimensional data encoding device can encode aplurality of pieces of attribute information at a high speed.

FIG. 153 is a flowchart of the attribute information encoding process(S7063). First, the three-dimensional data encoding device generates acoding coefficient from attribute information by Haar transformation(S7071).

The three-dimensional data encoding device then applies quantization tothe coding coefficient (S7072). The three-dimensional data encodingdevice then generates encoded attribute information (bitstream) byencoding the quantized coding coefficient (S7073).

The three-dimensional data encoding device then determines ΔQP (S7074).Note that the method of determining ΔQP is the same as step S7019 in thecase where LoD layers are used. Determined ΔQP is used for determining aquantization parameter used for quantization of a subsequent codingcoefficient.

The three-dimensional data encoding device applies inverse quantizationto the quantized coding coefficient (S7075). The three-dimensional dataencoding device then decodes the attribute information by applyinginverse Haar transformation to the inverse-quantized coding coefficient(S7076). For example, the decoded attribute information is referred toin the subsequent encoding.

FIG. 154 is a flowchart of a three-dimensional data decoding process inthe case where RAHT is used. First, the three-dimensional data decodingdevice decodes geometry information (geometry) from the bitstream(S7065). For example, the three-dimensional data decoding deviceperforms the decoding using an octree representation.

The three-dimensional data decoding device then decodes attributeinformation from the bitstream (S7066). For example, when thethree-dimensional data decoding device decodes a plurality of pieces ofattribute information, the three-dimensional data decoding device maysequentially decode the plurality of pieces of attribute information.For example, when the three-dimensional data decoding device decodescolor and reflectance as attribute information, the three-dimensionaldata decoding device decodes the result of encoding of color and theresult of encoding of reflectance in the order thereof in the bitstream.For example, if the result of encoding of color is followed by theresult of encoding of reflectance in the bitstream, thethree-dimensional data decoding device first decodes the result ofencoding of color and then decodes the result of encoding ofreflectance. Note that the three-dimensional data decoding device candecode the result of encoding of attribute information in the bitstreamin any order.

The three-dimensional data decoding device may obtain the informationindicating the starting point of the encoded data of each piece ofattribute information in the bitstream by decoding the header or thelike. In this way, the three-dimensional data decoding device canselectively decode attribute information that needs to be decoded, andtherefore can omit the decoding process for attribute information thatdoes not need to be decoded. Therefore, the processing amount of thethree-dimensional data decoding device can be reduced. Thethree-dimensional data decoding device may decode a plurality of piecesof attribute information in parallel, and integrate the results of thedecoding into one three-dimensional point cloud. In this way, thethree-dimensional data decoding device can decode a plurality of piecesof attribute information at a high speed.

FIG. 155 is a flowchart of the attribute information decoding process(S7066). First, the three-dimensional data decoding device decodes thecoding coefficient from the bitstream (S7081). The three-dimensionaldata decoding device then decodes ΔQP from the bitstream (S7082).Specifically, the three-dimensional data decoding device decodesDelta_Layer, ADelta_QP, and NumPointADelta from the header of thebitstream.

The three-dimensional data decoding device then applies inversequantization to the coding coefficient (S7083). In this inversequantization, a quantization parameter calculated using ΔQP obtained instep S7082 is used. The three-dimensional data decoding device thendecodes the attribute information by applying inverse Haartransformation to the inverse-quantized coding coefficient (S7084).

FIG. 156 is a block diagram of attribute information encoder 7023 in thecase where RAHT is used. Attribute information encoder 7023 includessorter 7081, Haar transformer 7082, quantizer 7083, inverse quantizer7084, inverse Haar transformer 7085, memory 7086, arithmetic encoder7087, and ΔQP calculator 7088.

Sorter 7081 generates a Morton code using geometry information on athree-dimensional point, and sorts a plurality of three-dimensionalpoints in the order of Morton codes. Haar transformer 7082 generates acoding coefficient by applying Haar transformation to attributeinformation. Quantizer 7083 quantizes the coding coefficient of theattribute information.

Inverse quantizer 7084 inverse-quantizes the quantized codingcoefficient. Inverse Haar transformer 7085 applies inverse Haartransformation to the coding coefficient. Memory 7086 stores values ofthe decoded attribute information on the plurality of three-dimensionalpoints. For example, the decoded attribute information on thethree-dimensional points stored in memory 7086 may be used forprediction or the like of a three-dimensional point yet to be encoded.

Arithmetic encoder 7087 calculates ZeroCnt from the quantized codingcoefficient, and arithmetically encodes ZeroCnt. Arithmetic encoder 7087also arithmetically encodes any quantized coding coefficient that is notzero. Arithmetic encoder 7087 may binarize the coding coefficient beforethe arithmetic encoding. Arithmetic encoder 7087 may generate and encodevarious kinds of head information.

ΔQP calculator 7088 determines values of Delta_Layer, ADelta_QP, andNumPointADelta from the actual code amount obtained by arithmeticencoder 7087 and the predetermined desired code amount. The quantizationby quantizer 7083 is performed using a quantization parameter based onthe determined Delta_Layer, ADelta_QP, and NumPointADelta. Arithmeticencoder 7087 arithmetically encodes Delta_Layer, ADelta_QP, andNumPointADelta and adds these values to the bitstream.

FIG. 157 is a block diagram of attribute information decoder 7043 in thecase where RAHT is used. Attribute information decoder 7043 includesarithmetic decoder 7091, inverse quantizer 7092, inverse Haartransformer 7093, and memory 7094.

Arithmetic decoder 7091 arithmetically decodes ZeroCnt and the codingcoefficient included in the bitstream. Arithmetic decoder 7091 maydecode various kinds of header information. Arithmetic decoder 7091 alsodecodes Delta_Layer, ADelta_QP, and NumPointADelta from the header ofthe bitstream.

Inverse quantizer 7092 inverse-quantizes the arithmetically decodedcoding coefficient. Specifically, inverse quantizer 7092 performs theinverse quantization using a quantization parameter based on the decodedDelta_Layer, ADelta_QP, and NumPointADelta.

Inverse Haar transformer 7093 applies inverse Haar transformation to theinverse-quantized coding coefficient. Memory 7094 stores values of thedecoded attribute information on the plurality of three-dimensionalpoints. For example, the decoded attribute information on thethree-dimensional points stored in memory 7094 may be used forprediction of a three-dimensional point yet to be decoded.

In the following, a variation of this embodiment will be described. Thethree-dimensional data encoding device may encode a quantizationparameter of attribute information on each three-dimensional point asnew attribute information.

In the following, an example of a process performed by thethree-dimensional data encoding device in this case will be described.The three-dimensional data encoding device encodes attribute informationA (such as color) by calculating a quantization parameter according tothe flow shown in FIG. 143 . In this process, as a new attribute valueof each three-dimensional point, the three-dimensional data encodingdevice encodes the quantization parameter used. In this case, thethree-dimensional data encoding device may perform the encoding bychanging the value of the quantization parameter for eachthree-dimensional point. For example, if the cumulative code amount isgreater than the value of the desired code amount×TH1, thethree-dimensional data encoding device can set the value of thequantization parameter to be greater, in order to reduce the actual codeamount. If the cumulative code amount is smaller than the value of thedesired code amount×TH3, the three-dimensional data encoding device canset the value of the quantization parameter to be smaller, in order toincrease the actual code amount.

After the encoding of attribute information A, the three-dimensionaldata encoding device encodes the quantization parameter assigned to eachthree-dimensional point as new attribute information A′. In thisprocess, the three-dimensional data encoding device may apply losslessencoding to prevent losing of the amount of information on thequantization parameters. The three-dimensional data encoding device mayadd, to the header or the like, information that indicates that theencoded attribute information is a quantization parameter. In this way,the three-dimensional data decoding device can properly decode thequantization parameter used by the three-dimensional data encodingdevice.

When performing predictive encoding of attribute information using Nthree-dimensional points in the periphery of a current three-dimensionalpoint, the three-dimensional data encoding device may encode aquantization parameter on the supposition that N=1. In this way, thecalculation amount can be reduced.

Next, an example of a process performed by the three-dimensional datadecoding device will be described. First, the three-dimensional datadecoding device decodes attribute information A′ among the attributeinformation in the bitstream to obtain a quantization parameter used forthe decoding of attribute information A. The three-dimensional datadecoding device then decodes attribute information A using the decodedquantization parameter.

Note that the three-dimensional data encoding device may encode, as newattribute information A′, ΔQP, which is the amount of change of thequantization parameter between three-dimensional points, instead of thequantization parameter described above. When ΔQP assumes a positive ornegative value, the three-dimensional data encoding device may transformsigned ΔQP into a positive value before encoding ΔQP as described below.When signed ΔQP (deltaQP_s) is smaller than 0, unsigned ΔQP (deltaQP_u)is set to be −1−(2×deltaQP_s). When signed ΔQP (deltaQP_s) is equal toor greater than 0, unsigned ΔQP (deltaQP_u) is set to be 2×deltaQP_s.

The three-dimensional data encoding device may encode, as attributeinformation, a quantization parameter used for encoding of eachattribute information. For example, the three-dimensional data encodingdevice may encode a quantization parameter of attribute information A oncolor as attribute information A′, and encode a quantization parameterof attribute information B on reflectance as attribute information B′.In this way, the quantization parameter can be changed for eachattribute information. For example, if the quantization parameter ofattribute information having higher priority is set to be smaller, andthe quantization parameter of attribute information having lowerpriority is set to be greater, the total code amount can be reducedwhile preserving the attribute information having higher priority.

When quantizing and encoding a prediction residual for attributeinformation on a three-dimensional point, if delta_Layer_present_flag,additional_delta_QP_present_flag and the like indicate that Delta_Layerand ADelta_QP are set in the header, the three-dimensional data encodingdevice need not use the value of a quantization weight (QW) thatindicates the importance of a three-dimensional point. For example, whenQW is used, the quantization parameter is set to be smaller when QW isgreater (the importance is higher). In this way, it can be chosenwhether to perform the quantization based on the importance determinedby an internal process such as prediction or based on a value set in theheader by the user, so that the two manners can be selectively useddepending on the purpose of the user.

The three-dimensional data encoding device may add, to the header, aflag that indicates whether to use the value of the quantization weight(QW) or not. In this way, it can be chosen whether to perform thequantization by combining the values of Delta_Layer and ADelta_QP and QWor not, the two manners can be selectively used depending on the purposeof the user.

When quantizing and encoding a transformation coefficient for attributeinformation on a three-dimensional point using RAHT or the like, ifdelta_Layer_present_flag, additional_delta_QP_present_flag and the likeindicate that Delta_Layer and ADelta_QP are set in the header, thethree-dimensional data encoding device need not use the value of thequantization weight (QW). In this way, it can be chosen whether toperform the quantization based on the importance determined by aninternal process such as prediction or based on a value set in theheader by the user, so that the two manners can be selectively useddepending on the purpose of the user. Furthermore, the three-dimensionaldata encoding device may add, to the header, a flag that indicateswhether to use the value of quantization weight (QW) or not. In thisway, it can be chosen whether to perform the quantization by combiningthe values of Delta_Layer and ADelta_QP and QW or not, the two mannerscan be selectively used depending on the purpose of the user.

FIG. 158 is a diagram showing a syntax example of an attributeinformation header (attribute header information) in this case. Theattribute information header shown in FIG. 158 differs from theattribute information header shown in FIG. 141 in that the attributeinformation header further includes default delta_Layer_present_flag,default_delta_Layer_index, default_additional_delta_QP_present_flag, anddefault_additional_delta_QP_index.

default_delta_Layer_present_flag is a flag that indicates whether to usean initially set value of Delta_Layer defined by a standard or the likeor not. For example, a value of 1 indicates that initially setDelta_Layer is to be used. A value of 0 indicates that initially setDelta_Layer is not to be used. In the case of the value of 0, thethree-dimensional data decoding device performs the following decodingprocess by setting Delta_Layer to be 0, for example.

default_delta_Layer_index is information that allows identification ofDelta_Layer to be used among one or more initially set values ofDelta_Layer defined by a standard or the like. For example,default_delta_Layer_index is defined as described below.

When default_delta_Layer_index=0, Delta_Layer for all layers is set tobe 1. That is, the value of the quantization parameter increases by 1every time a layer is incremented. When default_delta_Layer_index=1,Delta_Layer for all layers is set to be 2. That is, the value of thequantization parameter increases by 2 every time a layer is incremented.

If an initially set Delta_Layer is defined by a standard or the like inthis way, the quantization parameter can be changed without adding thevalue of Delta_Layer to the header, so that the code amount of theheader can be reduced.

default_additional_delta_QP_present_flag is a flag that indicateswhether to use an initially set value of ADelta_QP defined by a standardor the like or not. For example, a value of 1 indicates that initiallyset ADelta_QP is to be used. A value of 0 indicates that initially setADelta_QP is not to be used. In the case of the value of 0, thethree-dimensional data decoding device performs the following decodingprocess by setting ADelta_QP to be 0, for example.

default_additional_delta_QP_index is information that allowsidentification of ADelta_QP to be used among one or more values ofinitially set ADelta_QP defined by a standard or the like. For example,default_additional_delta_QP_index is defined as described below.

When default_additional_delta_QP_index=0, ADelta_QP is set to be 1 everyN three-dimensional points. That is, the value of the quantizationparameter increases by 1 each time N three-dimensional points areencoded or decoded. Note that the three-dimensional data encoding devicemay additionally add information indicating N to the header.

When default_additional_delta_QP_index=1, ADelta_QP is set to be 2 everyN three-dimensional points. That is, the value of the quantizationparameter increases by 2 each time N three-dimensional points areencoded or decoded. Note that the three-dimensional data encoding devicemay additionally add information indicating N to the header.

If an initially set ADelta_QP is defined by a standard or the like inthis way, the quantization parameter can be changed without adding thevalue of ADelta_QP to the header, so that the code amount of the headercan be reduced.

As described above, the three-dimensional data encoding device accordingto this embodiment performs the process shown in FIG. 159 . Thethree-dimensional data encoding device calculates a plurality ofcoefficient values (such as prediction residuals or coding coefficients)from a plurality of pieces of attribute information on a plurality ofthree-dimensional points included in point cloud data (S7091). Thethree-dimensional data encoding device then generates a plurality ofquantized values by quantizing each of the plurality of coefficientvalues (S7092). The three-dimensional data encoding device thengenerates a bitstream including the plurality of quantized values(S7093). The plurality of coefficient values belong to any of aplurality of layers (such as LoD layers or RAHT layers). In thequantization (S7092), the three-dimensional data encoding devicequantizes each of the plurality of coefficient values using aquantization parameter for the layer to which the coefficient valuebelongs. The bitstream includes first information (such as QPbase) thatindicates a reference quantization parameter and a plurality of piecesof second information (such as Delta_Layer[i]) for calculating aplurality of quantization parameters for the plurality of layers fromthe reference quantization parameter.

With such a configuration, the three-dimensional data encoding devicecan change the quantization parameter for each layer, and therefore canproperly perform the encoding. In addition, since the three-dimensionaldata encoding device encodes the first information that indicates areference quantization parameter and the second information forcalculating a plurality of quantization parameters from the referencequantization parameter, the coding efficiency can be improved.

For example, each of the plurality of pieces of second informationindicates the difference between the reference quantization parameterand the quantization parameter for the layer.

For example, the bitstream further includes a first flag(delta_Layer_present_flag) that indicates whether the plurality ofpieces of second information is included in the bitstream or not.

For example, the bitstream further includes third information (such asNumLayer) that indicates the number of the plurality of pieces of secondinformation included in the bitstream.

For example, the plurality of three-dimensional points are classifiedinto any of a plurality of layers (such as LoDs) based on the geometryinformation on the plurality of three-dimensional points.

For example, the plurality of coefficient values are generated bysorting each of the plurality of pieces of attribute information into ahigher frequency component and a lower frequency component to beclassified into any of the layers (such as RAHT layers).

For example, the three-dimensional data encoding device includes aprocessor and a memory, and the processor performs the process describedabove using the memory.

The three-dimensional data decoding device according to this embodimentperforms the process shown in FIG. 160 . The three-dimensional datadecoding device calculates quantization parameters for a plurality oflayers based on (i) first information (such as QPbase) that indicates areference quantization parameter and (ii) a plurality of pieces ofsecond information (such as Delta_Layer[i]) for calculating a pluralityof quantization parameters for a plurality of layers from the referencequantization parameter that are included in the bitstream (S7095).

The three-dimensional data decoding device then generates a plurality ofcoefficient values (such as prediction residuals or coding coefficients)by inverse-quantizing each of the plurality of quantized values includedin the bitstream using the quantization parameter for the layer to whichthe quantized value belongs among the calculated quantization parametersfor the plurality of layers (S7096). The three-dimensional data decodingdevice then calculates a plurality of pieces of attribute information ona plurality of three-dimensional points included in point cloud datafrom the plurality of coefficient values (S7097).

With such a configuration, the three-dimensional data decoding devicecan change the quantization parameter for each layer, and therefore canproperly perform the decoding. In addition, the three-dimensional datadecoding device can properly decode the bitstream encoded with a codingefficiency improved by using the first information that indicates areference quantization parameter and the plurality of pieces of secondinformation for calculating a plurality of quantization parameters fromthe reference quantization parameter.

For example, each of the plurality of pieces of second informationindicates the difference between the reference quantization parameterand the quantization parameter for the layer.

For example, the bitstream further includes a first flag(delta_Layer_present_flag) that indicates whether the plurality ofpieces of second information is included in the bitstream or not.

For example, the bitstream further includes third information (such asNumLayer) that indicates the number of the plurality of pieces of secondinformation included in the bitstream.

For example, the plurality of three-dimensional points are classifiedinto any of a plurality of layers (such as LoDs) based on the geometryinformation on the plurality of three-dimensional points.

For example, the plurality of coefficient values are generated bysorting each of the plurality of pieces of attribute information into ahigher frequency component and a lower frequency component to beclassified into any of the layers (such as RAHT layers).

For example, the three-dimensional data decoding device includes aprocessor and a memory, and the processor performs the process describedabove using the memory.

Embodiment 14

To achieve high compression, attribute information included in PointCloud Compression (PCC) data is transformed in a plurality of methods,such as Lifting, Region Adaptive Hierarchical Transform (RAHT) and othertransformation methods. Here, Lifting is one of transformation methodsusing Level of Detail (LoD).

Important signal information tends to be included in a low frequencycomponent, and therefore the code amount is reduced by quantizing a highfrequency component. That is, the transformation process has strongenergy compression characteristics.

Here, different quantization parameters may be used for differenttransformation schemes, in accordance with the characteristics of thetransformation coefficient. In this embodiment, an approach thatimproves efficiency by controlling transformation or quantization inaccordance with the attribute type or transformation scheme will bedescribed.

FIG. 161 is a block diagram showing a configuration of athree-dimensional data encoding device according to this embodiment. Thethree-dimensional data encoding device includes subtractor 7901,transformer 7902, transformation matrix holder 7903, quantizer 7904,quantization controller 7905, and entropy encoder 7906.

Subtractor 7901 calculates a coefficient value that is the differencebetween input data and reference data. For example, the input data isattribute information included in point cloud data, and the referencedata is a predicted value of the attribute information.

Transformer 7902 performs a transformation process on the coefficientvalue. For example, the transformation process is a process ofclassifying items of attribute information into LoDs. Note that thetransformation process may be Haar transformation or the like.Transformation matrix holder 7903 holds a transformation matrix used forthe transformation process by transformer 7902. For example, thetransformation matrix is a Haar transformation matrix. Thethree-dimensional data encoding device may selectively use any of thesetwo kinds of transformation processes. The three-dimensional dataencoding device may change the transformation process to be used foreach predetermined processing unit.

Quantizer 7904 quantizes the coefficient value to generate a quantizedcoefficient. Quantization controller 7905 controls a scale value(referred to also as a quantization step) used for quantization byquantizer 7904. For example, quantization controller 7905 controls thescale value based on at least one of the QP (quantization parameter),the type (such as color or reflectance) of attribute information, andthe transformation scheme (such as LoD or RAHT (Haar transformation)).

Entropy encoder 7906 entropy-encodes (arithmetically encodes, forexample) the quantized coefficient to generate a bitstream.

FIG. 162 is a block diagram showing a configuration of athree-dimensional data decoding device according to this embodiment. Thethree-dimensional data decoding device includes entropy decoder 7911,inverse quantizer 7912, quantization controller 7913, inversetransformer 7914, transformation matrix holder 7915, and adder 7916.

Entropy decoder 7911 decodes the quantized coefficient and the QP fromthe bitstream. Inverse quantizer 7912 inverse-quantizes the quantizedcoefficient to generate the coefficient value. Quantization controller7913 controls the scale value to be used by inverse quantizer 7912 basedon at least the QP, the type of attribute, the transformation scheme andthe like.

Inverse transformer 7914 inverse-transforms the coefficient value. Forexample, inverse transformer 7914 performs an inverse Haartransformation on the coefficient value. Transformation matrix holder7915 holds a transformation matrix used for the inverse transformationprocess by inverse transformer 7914. For example, the transformationmatrix is an inverse Haar transformation matrix.

Adder 7916 adds the reference data to the coefficient value to generateoutput data. For example, the output data is attribute informationincluded in point cloud data, and the reference data is a predictedvalue of the attribute information.

Here, a relationship between the coefficient value, which is data yet tobe quantized, and the quantized coefficient, which is the quantizedcoefficient value, is expressed by the following formula using the scalevalue (Scale).Quantized coefficient=Coefficient value/ScaleCoefficient value=Quantized coefficient×Scale

That is, in the quantization, the three-dimensional data encoding devicecalculates the quantized coefficient by dividing the coefficient valueby the scale value. In the inverse quantization, the three-dimensionaldata decoding device calculates the coefficient value by multiplying thequantized coefficient by the scale value.

A relationship between the quantization parameter (QP) and the scalevalue (Scale) is expressed by the following formula, for example. Notethat QP is referred to as a quantization value.QP=log(Scale)

Note that, as a relationship between QP and the scale value, thefollowing formulas used for Advanced Video Coding (AVC) and HighEfficiency Video Coding (HEVC) may be used. Furthermore, formulasobtained by adding a predetermined calculation to the following formulascan also be used.Scale=2^((QP-4)/6)QP=log₂(Scale×6)+4

The three-dimensional data encoding device stores the QP correspondingto the scale value used for the quantization in metadata in the encodedbitstream. The three-dimensional data decoding device decodes the QPfrom the bitstream, derives the scale value corresponding to the decodedQP, and uses the derived scale value for the inverse quantization.

Note that the three-dimensional data encoding device may first determinethe QP transmitted to the three-dimensional data encoding device, andthen transform the QP into a scale value and perform the quantizationusing the obtained scale value, or may first determine the scale valueand then transform the scale value into a QP and store the obtained QPin the bitstream. By transmitting the QP transformed from the scalevalue, the number of bits of the metadata can be reduced.

FIG. 163 is a diagram showing a configuration of a three-dimensionaldata encoding device according to this embodiment. FIG. 164 is a diagramshowing a configuration of a three-dimensional data decoding deviceaccording to this embodiment. FIG. 163 and FIG. 164 mainly showprocessors involved with encoding and decoding of attribute information,respectively.

As shown in FIG. 163 , three-dimensional data encoding device 7920includes attribute information encoders 7921 and 7922. Attributeinformation encoder 7921 and attribute information encoder 7922 supportdifferent of attribute information encoding schemes (different attributeinformation encoding methods). For example, attribute informationencoder 7921 and attribute information encoder 7922 use differenttransformation schemes, control parameters or the like. For example,three-dimensional data encoding device 7920 selects the attributeinformation encoding scheme to be used in accordance with an indicationor setting from the outside or attribute information to be processed.Three-dimensional data encoding device 7920 may add, to the bitstream,information that indicates the used attribute information encodingscheme.

As shown in FIG. 164 , three-dimensional data decoding device 7930includes attribute information decoders 7931 and 7932. For example,attribute information decoder 7931 and attribute information decoder7932 use different transformation schemes, control parameters or thelike. For example, three-dimensional data decoding device 7930 selectsthe attribute information encoding scheme (attribute informationdecoding scheme) to be used, based on the information indicating theattribute information encoding scheme used for encoding of the encodedattribute information added to the bitstream. Three-dimensional datadecoding device may select the attribute information encoding scheme(attribute information decoding scheme) to be used in accordance with anindication or setting from the outside or encoded attribute informationto be processed.

Three-dimensional data encoding device 7920 may adaptively change theencoding scheme to be used in accordance with the scheme of content orthe precision of encoding. Three-dimensional data encoding device 7920may adaptively change the encoding scheme for attribute informationincluded in one content on a predetermined unit basis. Three-dimensionaldata encoding device 7920 may use any one attribute information encodingscheme or use a plurality of encoding schemes at the same time.

FIG. 165 is a block diagram showing a configuration of attributeinformation encoder 7921. Attribute information encoder 7921 includesleft bit shifter 7941, transformer 7942, right bit shifter 7943, scalevalue calculator 7944, quantizer 7945, and entropy encoder 7946.

Left bit shifter 7941 generates shifted attribute information byshifting bits to the left, in other words, performing a left bit shift(carry-up) on attribute information. Transformer 7942 generates ashifted coefficient value by performing a transformation process on theshifted attribute information using geometry information on a pluralityof three-dimensional points. Here, the transformation process is Liftingusing LoD or RAHT. Right bit shifter 7943 generates a coefficient valueby shifting bits to the right, in other words, performing a right bitshift (carry-down) on the shifted coefficient value. Here, the number ofbits shifted in the right bit shift is the same as the number of bitsshifted in the left bit shift. In this way, the three-dimensional dataencoding device carries up the attribute information by left bit shiftbefore the transformation process, performs the transformation processon the carried-up attribute information (shifted attribute information),and carries down the obtained coefficient value (shifted coefficientvalue) by right bit shift. In this way, the calculation precision of thetransformation process can be improved, so that the encoding efficiencycan be improved.

Scale value calculator 7944 transforms a QP into a scale value.Quantizer 7945 generates a quantized coefficient by quantizing acoefficient value using the scale value. Entropy encoder 7946 generatesencoded attribute information (bitstream) by entropy-encoding the QP andthe quantized coefficient.

Note that, although the configuration of attribute information encoder7922 is generally the same as attribute information encoder 7921,attribute information encoder 7922 differs from attribute informationencoder 7921 in transformation scheme used by transformer 7942, controlparameter used in each processor and the like.

FIG. 166 is a block diagram showing a configuration of attributeinformation decoder 7931. Attribute information decoder 7931 includesentropy decoder 7951, scale value calculator 7952, inverse quantizer7953, left bit shifter 7954, inverse transformer 7955, and right bitshifter 7956.

Entropy decoder 7951 generates a quantized coefficient byentropy-decoding encoded attribute information. Scale value calculator7952 transforms a QP decoded from the bitstream into a scale value.Inverse quantizer 7953 generates a coefficient value byinverse-quantizing the quantized coefficient using the scale value.

Left bit shifter 7954 generates a shifted coefficient value byperforming a left bit shift (carry-up) on the coefficient value. Inversetransformer 7955 generates shifted attribute information by performingan inverse transformation process on the shifted coefficient value usingdecoded geometry information, which is geometry information on aplurality of three-dimensional points decoded from the bitstream. Here,the inverse transformation process is an inverse transformation processto the transformation process performed by transformer 7942 included inattribute information encoder 7921, and is an inverse transformationprocess to Lifting using LoD or RAHT, for example. Right bit shifter7956 generates decoded attribute information by performing a right bitshift (carry down) on the shifted attribute information. Here, thenumber of bits shifted in the right bit shift is the same as the numberof bits shifted in the left bit shift.

Note that, although the configuration of attribute information decoder7932 is generally the same as attribute information decoder 7931,attribute information decoder 7932 differs from attribute informationdecoder 7931 in transformation scheme used by transformer 7955, controlparameter used in each processor and the like.

FIG. 167 is a block diagram showing another example configuration of thethree-dimensional data decoding device. The three-dimensional datadecoding device shown in FIG. 167 includes attribute informationdecoders 7931A and 7932A, and scale value calculator 7952A.

Attribute information decoders 7931A and 7932A differ in transformationscheme or control parameter, for example, as with attribute informationdecoders 7931 and 7932 described above. The functions of entropy decoder7951A, inverse quantizer 7953A, left bit shifter 7954A, inversetransformer 7955A, and right bit shifter 7956A included in attributeinformation decoder 7931A are the same as the functions of entropydecoder 7951, inverse quantizer 7953, left bit shifter 7954, inversetransformer 7955, and right bit shifter 7956. The functions of entropydecoder 7951B, inverse quantizer 7953B, left bit shifter 7954B, inversetransformer 7955B, and right bit shifter 7956B included in attributeinformation decoder 7931B are the same as the functions of entropydecoder 7951, inverse quantizer 7953, left bit shifter 7954, inversetransformer 7955, and right bit shifter 7956.

Scale value calculator 7952A calculates a scale value (Scale1) used byinverse quantizer 7953A and a scale value (Scale2) used by inversequantizer 7953B based on the QP using table 7960A (quantization table).

Table 7960A is a table that indicates a correspondence between aplurality of values of QP and a plurality of values of a scale value. Byusing table 7960A in this way, the processing amount can be reducedcompared with the case where the transformation is performed using atransformation formula.

The table used for calculation of Scale1 and the table used forcalculation of Scale2 may be different or the same. When the same tableis used, the memory can be saved since a common table is used for twotransformation schemes (encoding schemes).

Number of bits A1 (shift amount) of the bit shift by left bit shifter7954A and right bit shifter 7956A and number of bits A2 of the bit shiftby left bit shifter 7954B and right bit shifter 7956B may be the same ordifferent.

FIG. 168 is a block diagram showing another example configuration of thethree-dimensional data decoding device. The three-dimensional datadecoding device shown in FIG. 168 includes attribute informationdecoders 7931B and 7932B, and scale value calculator 7952B.

Attribute information decoder 7931B differs from attribute informationdecoder 7931A in that attribute information decoder 7931B includesinverse quantizer 7953C instead of inverse quantizer 7953A and left bitshifter 7954A. Attribute information decoder 7932B differs fromattribute information decoder 7932A in that attribute informationdecoder 7932B includes inverse quantizer 7953D instead of inversequantizer 7953B and left bit shifter 7954B.

In the example shown in FIG. 168 , table 7960B for transforming a QPinto a scale value is merged with the left bit shift process before thetransformation process for each attribute encoding. Note that table7960B may be provided for each encoding scheme or may be shared betweena plurality of encoding schemes.

That is, inverse quantizer 7953C performs both the inverse quantizationprocess performed by inverse quantizer 7953A and the left bit shiftperformed by left bit shifter 7954A shown in FIG. 167 by multiplying thequantized coefficient obtained by entropy decoder 7951A by a scale value(Scale3) generated by scale value calculator 7952B. Similarly, inversequantizer 7953D performs both the inverse quantization process performedby inverse quantizer 7953B and the left bit shift performed by left bitshifter 7954B shown in FIG. 167 by multiplying the quantized coefficientobtained by entropy decoder 7951B by a scale value (Scale4) generated byscale value calculator 7952B.

Scale value calculator 7952B calculates the scale value (Scale3) used byinverse quantizer 7953C and the scale value (Scale4) used by inversequantizer 7953D based on the QP using table 7960B. Table 7960B is atable that indicates a correspondence between a plurality of values ofQP and a plurality of values of a scale value.

FIG. 169 is a diagram showing an example of table 7960B. This drawingshows a correspondence between QP and Scale3 in a case where A1=8 bits.Although this drawing show a correspondence between QP and the scalevalue (Scale1) in a case where the table is not merged with the left bitshift for reference, this information need not be included in table7960B. Note that table 7960A used by scale value calculator 7952A shownin FIG. 167 indicates the correspondence between QP and Scale1 shown inFIG. 169 .

As shown in FIG. 169 , Scale3 is calculated by multiplying Scale1 by2^(A1) and rounding the product. Instead of the rounding, rounding up,rounding down, or approximation may be used. The value of A1 may be afixed value or a variable value. In this way, the value of Scale3 in thecase where the table is merged with the left bit shift process can becalculated.

The same holds true for the correspondence between QP and Scale4. Notethat, when number of bits A2 differs from number of bits A1, a tableincluding values of Scale4 calculated based on number of bits A2 isseparately created, and Scale4 is calculated from QP using the table.

In this way, by making the value of Scale4 an integer by rounding or thelike, the processing amount can be reduced. Furthermore, by merging thetable with the left bit shift process, the resolution of Scale×2^(A1) isimproved, the error due to the round calculation can be reduced.

In the example shown in FIG. 169 , table 7960B includes six values of 0to 5 as QP values. However, the QP value can be a value greater than orequal to 6. Scale value calculator 7952B derives Scale3 (or Scale4) fromQP according to the following formula.Scale3=Table(QP %6)>>(QP/6)

Here, QP %6 represents the remainder of the division of QP by 6, andQP/6 represents the quotient of the division of QP by 6. Table(N)indicates the value of Scale3 that corresponds to value N of QP in thetable shown in FIG. 169 . For example, when QP is 8, QP %6 is 2.Therefore, Table(QP %6) is “203”, which is the value in the case whereQP=2 shown in FIG. 169 . Furthermore, QP/6 is 1, and therefore, thevalue obtained by the left bit shift of “203” by 1 bit is calculated asScale3. In this way, by combining table and calculation, Scale3corresponding to all QP values including those in the case where QP isgreater than or equal to 6 can be calculated.

Note that table 7960B may indicate scale values corresponding to allpossible values of QP. In that case, the calculation described above isnot performed, and values corresponding to values of QP are defined asScale3 in table 7960B.

In the following, an example case where common table 7960B is used fortwo encoding schemes in the case where numbers of bits A1 and A2 of thebit shifts are different will be described. FIG. 170 is a block diagramshowing a configuration of scale value calculator 7952B in this case.Scale value calculator 7952B includes common calculator 7961 and bitshifter 7962.

Table 7960B is a table created based on the bit shift amount of any oneattribute encoding. In this example, table 7960B is a table createdbased on A1 bits. Common calculator 7961 transforms QP into Scale3 forattribute information decoder 7931B using table 7960B. Common calculator7961 also transforms QP into Scale3A using table 7960B. Here, when thevalues of QP are the same, Scale3A is the same value as Scale3.

Bit shifter 7962 generates Scale4 for attribute information decoder7932B by performing a left bit shift of Scale3A by (A2−A1) bits, when(A2−A1)>0. Note that, when (A2−A1)<0, bit shifter 7962 generates Scale4by performing a right bit shift of Scale3A by (A1−A2) bits.

In the following, the bit shift by number of bits A1 or A2 merged withtable 7960B will be referred to as a first bit shift, and the bit shiftperformed by bit shifter 7962 will be referred to as a second bit shift.That is, Scale3 and Scale4 are expressed by the following formulas.Scale3=Table(QP %6)>>(QP/6)Scale4=Table(QP %6)>>(QP/6)>>(A2−A1)

The attribute encoding scheme on which the creation of table 7960B isbased can be any scheme and may be the encoding scheme used by attributeinformation decoder 7932B. Furthermore, three or more attributeinformation encoding schemes may be used.

When three attribute encoding schemes are used, for example, if thenumbers of bits (shift amounts) of the first bit shift for the threeattribute information encoding schemes are A1, A2, and A3, the number ofbits of the values in the table can be minimized by creating the tableusing the minimum of the numbers of bits of A1, A2, and A3. On the otherhand, if the table is created by using the maximum of the numbers ofbits A1, A2, and A3, the error due to the round calculation can bereduced, and therefore, the precision can be improved. If the table iscreated by using the middle of the numbers of bits A1, A2, and A3, theadvantages described above can be combined.

When the number of bits (A1 or A2) of the first bit shift is a fixedvalue, the number of bits of the second bit shift may be fixed. WhenA1=A2, the second bit shift need not be performed. A table that is notmerged with the first bit shift may be shared, and A1=0 in that case.

In the following, exceptional processes will be described. Thethree-dimensional data decoding device may set Scale=1 when Scale<1,Scale being calculated according to the following expression concerningQP and Scale.Scale=2^((QP-4)/6)

The three-dimensional data decoding device may set Scale=1 when QP<4,that is, QP=0, 1, 2, or 3, QP being calculated according to thefollowing expression concerning QP and Scale. In other words, theminimum value of QP is 4, and the three-dimensional data decoding devicemay assume QP=4 when QP is smaller than 4.Scale=Table(QP %6)>>(QP/6)

In the following relationship concerning QP and Scale, the valuesubtracted from QP need not be 4 but can be arbitrary value x. In thatcase, the three-dimensional data decoding device set Scale=1 when QP<x.Scale=2^((QP-4)/6)

Note that the same process may be performed when Scale is calculatedfrom QP in the three-dimensional data encoding device.

When QP and Scale are related according to a formula different from theformulas described above, again, the number of bits of QP to betransmitted can be reduced by using the processes described in thisembodiment. The processing amount involved with the quantization orinverse quantization can be reduced.

Next, a process performed by the three-dimensional data encoding devicewill be described. The process by the three-dimensional data encodingdevice is similar to the process by the three-dimensional data decodingdevice. FIG. 171 is a block diagram showing a configuration of thethree-dimensional data encoding device. The three-dimensional dataencoding device includes attribute information encoders 7921A and 7922A,and scale value calculator 7944A.

As with attribute information decoders 7931B and 7932B, attributeinformation encoders 7921A and 7922A differ in transformation scheme orattribute type to be processed. The functions of left bit shifter 7941A,transformer 7942A, and entropy encoder 7946A included in attributeinformation encoder 7921A are the same as the functions of left bitshifter 7941, transformer 7942, and entropy encoder 7946. The functionsof left bit shifter 7941B, transformer 7942B, and entropy encoder 7946Bincluded in attribute information encoder 7922A are the same as thefunctions of left bit shifter 7941, transformer 7942, and entropyencoder 7946.

Scale value calculator 7944A calculates a scale value (Scale3) used byquantizer 7945A and a scale value (Scale4) used by quantizer 7945B basedon the QP using table 7970A. Table 7970A is a table that indicates acorrespondence between a plurality of values of QP and a plurality ofvalues of a scale value. For example, table 7970A is the same as table7960B. For example, the process performed by scale value calculator7944A is the same as the process performed by scale value calculator7952B.

Quantizer 7945A performs both the right bit shift performed by right bitshifter 7943 and the quantization process performed by quantizer 7945shown in FIG. 165 by dividing the shifted coefficient value obtained bytransformer 7942A by the scale value (Scale3) generated by scale valuecalculator 7952B. Similarly, quantizer 7945B performs both the right bitshift performed by right bit shifter 7943 and the quantization processperformed by quantizer 7945 shown in FIG. 165 by dividing the shiftedcoefficient value obtained by transformer 7942B by the scale value(Scale4) generated by scale value calculator 7952B.

FIG. 172 is a block diagram showing a configuration of scale valuecalculator 7944A. Scale value calculator 7944A includes commoncalculator 7971 and bit shifter 7972.

Table 7970A is a table created based on the bit shift amount for any oneattribute encoding. In this example, table 7970A is a table createdbased on A1 bits. Therefore, common calculator 7971 transforms QP intoScale3 using table 7970A. Common calculator 7971 also transforms QP intoScale3A using table 7970A. Here, when the values of QP are the same,Scale3A is the same value as Scale3.

Bit shifter 7972 generates Scale4 by performing a left bit shift ofScale3A by (A2−A1) bits, when (A2−A1)>0. Note that, when (A2−A1)<0, bitshifter 7972 generates Scale4 by performing a right bit shift of Scale3Aby (A1−A2) bits.

For example, relationships between the scale values and QP are the sameas those in the three-dimensional data decoding device, and expressed bythe following formulas.Scale3=Table(QP %6)>>(QP/6)Scale4=Table(QP %6)>>(QP/6)>>(A2−A1)

Note that table 7970A may be the same as or different from table 7960Bused in the three-dimensional data decoding device. For example, a tableconcerning QP and 1/Scale3 (or 1/Scale4) may be created, and thethree-dimensional data encoding device may multiply the shiftedcoefficient value by 1/Scale3. In that case, in order that the value of1/Scale3 is an integer, the table may contain a value obtained bymultiplying 1/Scale3 by 2^(T) and rounding (or rounding down, roundingup, or taking an approximation of) the product. That is, the value inthe table is expressed as follows.Value in Table=round((1/Scale)×2^(T))

In that case, the three-dimensional data encoding device divides thevalue obtained by the transformation using the table by 2^(T). In otherwords, the three-dimensional data encoding device performs a right bitshift by T bits.

FIG. 173 is a flowchart of a three-dimensional data decoding processperformed by the three-dimensional data decoding device. First, thethree-dimensional data decoding device obtains a quantized coefficientand a QP by entropy-decoding encoded attribute information (S7901). Thethree-dimensional data decoding device then calculates a scale value(S7902).

Specifically, the three-dimensional data decoding device transforms a QPinto a scale value using a table (S7911). When the encoding schemeapplied to the attribute information is not a first encoding scheme(that is, is a second encoding scheme) (if No in S7912), thethree-dimensional data decoding device performs a second bit shift,which is a left bit shift of the scale value obtained in S7911 (S7913).The shift amount of the second bit shift is (A2−A1) bits. Here, A1represents the shift amount of a first bit shift for the first encodingscheme, and A2 represents the shift amount of the first bit shift forthe second encoding scheme. Note that A1, A2, and A2−A1 may be fixedvalues determined in advance or variable values.

The three-dimensional data decoding device then performs the inversequantization and the left bit shift at the same time by multiplying thequantized coefficient obtained in step S7901 by the scale value obtainedin step S7913 (S7903).

On the other hand, when the encoding scheme applied to the attributeinformation is the first encoding scheme (if Yes in S7912), thethree-dimensional data decoding device performs the inverse quantizationand the left bit shift at the same time by multiplying the quantizedcoefficient obtained in step S7901 by the scale value obtained in stepS7911 (S7903).

Note that the process of calculating the scale value in thethree-dimensional data encoding device is similar to that describedabove. The three-dimensional data encoding device performs thequantization and the right bit shift at the same time by dividing theshifted coefficient value by the calculated scale value.

In the following, other examples will be described. Although an examplehas been described above in which a common table (quantization table)including a left bit shift (first bit shift) process is used for aplurality of attribute encoding schemes, and a second bit shift is usedas required, a common table may be used for a plurality of attributetypes, such as color and reflectance.

FIG. 174 is a block diagram showing a configuration of scale valuecalculator 7952C in that case. Scale value calculator 7952C includes bitshifters 7963 and 7964, in addition to the components of scale valuecalculator 7952B shown in FIG. 170 . Here, Scale3 is a scale value forcolor for the first encoding scheme, Scale4 is a scale value for colorfor the second encoding scheme, Scale5 is a scale value for reflectancefor the first encoding scheme, and Scale6 is a scale value forreflectance for the second encoding scheme. In addition, the shiftamount of the first bit shift is previously determined in accordancewith the encoding scheme and the attribute scheme. The shift amount is Xbits for the first encoding scheme, Y bits for the second encodingscheme, S bits for color, and T bits for reflectance.

In this case, common table 7960B is created based on the number of bitsfor color for the first encoding scheme. Common calculator 7961generates Scale3, Scale3A, and Scale3B using table 7960B. Here, Scale3,Scale3A, and Scale3B are the same value for the same value of QP. Bitshifter 7962 generates Scale4 and Scale4A by performing the second bitshift of Scale3A by (Y−X) bits. Here, Scale4 and Scale4A are the samevalue. Bit shifter 7963 generates Scale5 by performing a third bit shiftof Scale3B by (T−S) bits. Bit shifter 7964 generates Scale6 byperforming the third bit shift of Scale4A by (T−S) bits.

Note that although an example has been shown here in which the shiftamount (number of bits) is determined for each encoding scheme and foreach attribute type, the shift amount may be determined for eachcombination of encoding scheme and attribute type. The same approach canbe applied to that case.

When the shift amount is determined in accordance with a parameter ofthe attribute encoding scheme or the geometry information (geometry)encoding scheme, again, a common table can be used, and a bit shift by anumber of bits corresponding to a difference can be performed. That is,a common table can be used for a plurality of parameters of an encodingscheme. Furthermore, a common table can be used in the encoding ordecoding of geometry information.

Furthermore, a plurality of tables may be used. That is, a common tablemay be used for some of a plurality of quantization conditions. Forexample, a plurality of tables may be prepared for one attributeencoding scheme according to the difference in bit shift or calculationmethod, and may be selectively used. In that case, the three-dimensionaldata encoding device stores, in the metadata (such as the header of theattribute data), information that indicates which table has been used,and notifies the three-dimensional data decoding device of theinformation. The three-dimensional data decoding device determines whichtable has been used based on the information included in the metadata,and uses the table for the inverse quantization.

When both Lifting or RAHT, which can involve a quantization, and amethod such as Predicting that involves no quantization are used asmethods of encoding attribute information, a common table may be usedfor the method that involves a quantization and the method that involvesno quantization. In that case, for the method that involves noquantization, QP is set to be a value (such as a scale value=1) withwhich no quantization is performed. Alternatively, it is also possiblethat the three-dimensional data decoding device uses the common tablefor Lifting and RAHT that involves a quantization but does not use thecommon table for the method that involves no quantization. That is,whether to use the common table or not may be determined based onwhether to perform a quantization or not.

When the bit shift amounts, such as A1, A2, and A3, are variable, thethree-dimensional data encoding device may store the bit shift amountsor identifiers indicating the bit shift amounts in the metadata, andnotify the three-dimensional data decoding device of the bit shiftamounts. Note that the three-dimensional data encoding device maytransmit the shift amount of the second bit shift, such as (A2−A1), orthe shift amount of the third bit shift. (A2−A1) may be restricted to apositive value or negative value, or may assume both positive andnegative values.

Whether or not to merge the table for transforming QP values into scalevalues with the left bit shift process before the transformation may bechanged in accordance with the attribute information. FIG. 175 is ablock diagram showing a configuration of a three-dimensional datadecoding device in that case. The three-dimensional data decoding deviceincludes attribute information decoders 7931B and 7932A, and scale valuecalculators 7952A and 7952B.

In attribute information decoder 7931B, the first bit shift process ismerged. Scale value calculator 7952B has table 7960B that combines aninverse quantization and a left bit shift, and uses table 7960B toderive Scale3.

In attribute information decoder 7932A, the first bit shift process isnot merged. Scale value calculator 7952A has table 7960A for an inversequantization that is not merged with the first bit shift process, anduses table 7960A to derive Scale2.

Whether to merge the first bit shift process or not may be adaptivelychanged. For example, when a processing calculation other than theinverse quantization and the first bit shift is shared between theinverse transformation processes of the two attribute informationdecoders, the three-dimensional data decoding device may change tablesso that the encoding is optimally performed.

As described above, the three-dimensional data decoding device maychange whether to use the table merged with the first bit shift processor whether to perform the first bit shift process, in accordance withthe encoding scheme.

Which of the methods described above is used may be determined inadvance or be adaptively changed. When adaptively changing which of themethods described above is used, the three-dimensional data encodingdevice stores an identifier indicating which method has been used in themetadata, and transmits the identifier to the three-dimensional datadecoding device. Note that the method to be used may be changed inaccordance with the type of the attribute information or a parameter ofthe encoding.

A process similar to the process in the three-dimensional data decodingdevice described above may be used for a process in thethree-dimensional data encoding device. Note that the three-dimensionaldata encoding device may perform a process of transforming a QP into ascale value in the same manner as the process in the three-dimensionaldata decoding device or may perform an inverse transformation thereto (aprocess of transforming a scale value into a QP).

As described above, the three-dimensional data encoding device accordingto this embodiment performs the process shown in FIG. 176 . Thethree-dimensional data encoding device uses a first encoding scheme anda second encoding scheme, which is different from the first encodingscheme. For example, the first encoding scheme and the second encodingscheme are different attribute information encoding schemes. Forexample, the first encoding scheme and the second encoding scheme differin the scheme of the transformation process, a control parameter or thelike.

The three-dimensional data encoding device transforms a firstquantization parameter into a first scale value (such as Scale3) ortransforms a first scale value into a first quantization parameter usinga first table (such as table 7970A) that is common to the first encodingscheme and the second encoding scheme and indicates a correspondencebetween a plurality of values of the first quantization parameter (suchas QP) and a plurality of values of the first scale value (S7921).

The three-dimensional data encoding device then generates encodedattribute information by an encoding including a first quantizationprocess that divides each of a plurality of first coefficient values(such as shifted coefficient values) based on items of attributeinformation on a plurality of three-dimensional points included in pointcloud data by the first scale value (S7922). The three-dimensional dataencoding device then generates a bitstream including the encodedattribute information and the first quantization parameter (S7923).

With this, the three-dimensional data encoding device can use a commonfirst table for the first encoding scheme and the second encodingscheme. Therefore, the three-dimensional data encoding device can reducethe memory usage.

For example, in the encoding including the first quantization process(S7922), a bit shift to the left (left bit shift) is performed on eachof the items of attribute information to generate items of shiftedattribute information, and a transformation process using the items ofgeometry information on the plurality of three-dimensional points isperformed on the plurality of shifted attribute information to generatea plurality of first coefficient values. The first scale value is avalue obtained by multiplying a second scale value (such as Scale1) forquantization by a coefficient (such as 2^(A1)) corresponding to the bitshift to the left. The three-dimensional data encoding device divideseach of the plurality of first coefficient values by the first scalevalue, thereby performing a quantization and a bit shift to the right(right bit shift) by the same number of bits as the bit shift to theleft.

With this, the three-dimensional data encoding device can improve theprecision.

For example, the first number of bits (such as A1), which is the numberof bits shifted in the bit shift to the left and the bit shift to theright used in the first encoding scheme, and the second number of bits(such as A2), which is the number of bits shifted in the bit shift tothe left and the bit shift to the right used in the second encodingscheme, are different. In the transformation from a first scale value toa first quantization parameter or a first quantization parameter to afirst scale value (S7921), the three-dimensional data encoding device(i) determines the first quantization parameter (such as QP) by applyingthe first table to the first scale value (such as Scale3) or (ii)determines the first scale value (such as Scale3) by applying the firsttable to the first quantization parameter when the first encoding schemeis used, and (i) determines the first quantization parameter byperforming a bit shift of the first scale value (such as Scale4) by anumber of bits equal to the difference between the first number of bitsand the second number of bits and applying the first table to the firstscale value (such as Scale3A) resulting from the bit shift, or (ii)determines the third scale value (such as Scale3A) by applying the firsttable to the first quantization parameter and calculates the first scalevalue (Scale4) by performing a bit shift of the third scale value by anumber of bits equal to the difference between the first number of bitsand the second number of bits when the second encoding scheme is used.

With this, the three-dimensional data encoding device can use a commontable even when the number of bits shifted in the bit shift differsbetween the first encoding scheme and the second encoding scheme.Therefore, the three-dimensional data encoding device can reduce thememory usage.

For example, the three-dimensional data encoding device furthertransforms a second quantization parameter into a fourth scale value ortransforms a fourth scale value into a second quantization parameterusing a second table that is common to the first encoding scheme and thesecond encoding scheme and indicates a correspondence between aplurality of values of the second quantization parameter and a pluralityof values of the fourth scale value. The three-dimensional data encodingdevice generates encoded geometry information by an encoding including asecond quantization process that divides each of a plurality of secondcoefficient values based on the items of geometry information on theplurality of three-dimensional points by the fourth scale value. Thebitstream further includes the encoded geometry information and thesecond quantization parameter. That is, the three-dimensional dataencoding device may perform a control on the geometry information thatis similar to the control on the attribute information.

With this, the three-dimensional data encoding device can use a commonsecond table for the first encoding scheme and the second encodingscheme. Therefore, the three-dimensional data encoding device can reducethe memory usage.

For example, the three-dimensional data encoding device includes aprocessor and a memory, and the processor performs the process describedabove using the memory.

The three-dimensional data decoding device according to this embodimentperforms the process shown in FIG. 177 . The three-dimensional datadecoding device uses a first decoding scheme (first encoding scheme) anda second decoding scheme (second encoding scheme), which is differentfrom the first decoding scheme. The three-dimensional data decodingdevice obtains, from the bitstream, encoded attribute informationgenerated by encoding items of attribute information on a plurality ofthree-dimensional points included in point cloud data and a firstquantization parameter (such as QP) (S7931).

The three-dimensional data decoding device then transform the firstquantization parameter into a first scale value (such as Scale3) using afirst table (such as table 7960B) that is common to the first decodingscheme and the second decoding scheme and indicates a correspondencebetween a plurality of values of the first quantization parameter and aplurality of values of the first scale value (S7932).

The three-dimensional data decoding device then decodes the items ofattribute information (obtains decoded attribute information, forexample) by a decoding including a first inverse quantization processthat multiplies each of a plurality of first quantized coefficientsbased on encoded attribute information by the first scale value (S7933).

With this, the three-dimensional data decoding device can use a commonfirst table for the first decoding scheme and the second decodingscheme. Therefore, the three-dimensional data decoding device can reducethe memory usage.

For example, in the decoding including the first inverse quantizationprocess (S7933), the three-dimensional data decoding device generates aplurality of first coefficient values (such as shifted coefficientvalues) from a plurality of first quantized coefficients by the firstinverse quantization process, generates items of shifted attributeinformation by performing an inverse transformation process using itemsof geometry information on a plurality of three-dimensional points onthe plurality of first coefficient values, and generates items ofattribute information by performing a bit shift to the right (right bitshift) of each of the items of shifted attribute information. The firstscale value is a value obtained by multiplying a second scale value(such as Scale1) for inverse quantization by a coefficient (such as2^(A1)) corresponding to the bit shift to the right. By multiplying eachof the plurality of first quantized coefficients by the first scalevalue, a bit shift to the left (left bit shift) by the same number ofbits as the bit shift to the right and an inverse quantization areperformed.

With this, the three-dimensional data decoding device can improve theprecision.

For example, the first number of bits (such as A1), which is the numberof bits shifted in the bit shift to the right and the bit shift to theleft used in the first decoding scheme, and the second number of bits(such as A2), which is the number of bits shifted in the bit shift tothe right and the bit shift to the left used in the second decodingscheme, are different. In the transformation from a first quantizationparameter to a first scale value (S7932), the three-dimensional datadecoding device determines the first scale value (such as Scale3) byapplying the first table to the first quantization parameter (such asQP) when the first decoding scheme is used, and determines the thirdscale value (such as Scale3A) by applying the first table to the firstquantization parameter and calculates the first scale value (such asScale4) by performing a bit shift of the third scale value by a numberof bits equal to the difference between the first number of bits and thesecond number of bits when the second decoding scheme is used.

With this, the three-dimensional data decoding device can use a commontable even when the number of bits shifted in the bit shift differsbetween the first decoding scheme and the second decoding scheme.Therefore, the three-dimensional data decoding device can reduce thememory usage.

For example, the three-dimensional data decoding device further obtains,from the bitstream, encoded geometry information obtained by encodingitems of geometry information on the plurality of three-dimensionalpoints and a second quantization parameter. The three-dimensional datadecoding device transforms the second quantization parameter into afourth scale value using a second table that is common to the firstdecoding scheme and the second decoding scheme and indicates acorrespondence between a plurality of values of the second quantizationparameter and a plurality of values of the fourth scale value. Thethree-dimensional data decoding device decodes the items of geometryinformation by a decoding including a second inverse quantizationprocess that multiplies each of a plurality of second quantizedcoefficients based on the encoded geometry information by the fourthscale value. That is, the three-dimensional data decoding device mayperform a control on the geometry information that is similar to thecontrol on the attribute information.

With this, the three-dimensional data decoding device can use a commonsecond table for the first decoding scheme and the second decodingscheme. Therefore, the three-dimensional data decoding device can reducethe memory usage.

For example, when the first quantization parameter is smaller than 4,the three-dimensional data decoding device assumes the firstquantization parameter to be 4. With this, the three-dimensional datadecoding device can properly perform the decoding.

For example, the three-dimensional data decoding device includes aprocessor and a memory, and the processor performs the process describedabove using the memory.

A three-dimensional data encoding device, a three-dimensional datadecoding device, and the like according to the embodiments of thepresent disclosure have been described above, but the present disclosureis not limited to these embodiments.

Note that each of the processors included in the three-dimensional dataencoding device, the three-dimensional data decoding device, and thelike according to the above embodiments is typically implemented as alarge-scale integrated (LSI) circuit, which is an integrated circuit(IC). These may take the form of individual chips, or may be partiallyor entirely packaged into a single chip.

Such IC is not limited to an LSI, and thus may be implemented as adedicated circuit or a general-purpose processor. Alternatively, a fieldprogrammable gate array (FPGA) that allows for programming after themanufacture of an LSI, or a reconfigurable processor that allows forreconfiguration of the connection and the setting of circuit cellsinside an LSI may be employed.

Moreover, in the above embodiments, the structural components may beimplemented as dedicated hardware or may be realized by executing asoftware program suited to such structural components. Alternatively,the structural components may be implemented by a program executor suchas a CPU or a processor reading out and executing the software programrecorded in a recording medium such as a hard disk or a semiconductormemory.

The present disclosure may also be implemented as a three-dimensionaldata encoding method, a three-dimensional data decoding method, or thelike executed by the three-dimensional data encoding device, thethree-dimensional data decoding device, and the like.

Also, the divisions of the functional blocks shown in the block diagramsare mere examples, and thus a plurality of functional blocks may beimplemented as a single functional block, or a single functional blockmay be divided into a plurality of functional blocks, or one or morefunctions may be moved to another functional block. Also, the functionsof a plurality of functional blocks having similar functions may beprocessed by single hardware or software in a parallelized ortime-divided manner.

Also, the processing order of executing the steps shown in theflowcharts is a mere illustration for specifically describing thepresent disclosure, and thus may be an order other than the shown order.Also, one or more of the steps may be executed simultaneously (inparallel) with another step.

A three-dimensional data encoding device, a three-dimensional datadecoding device, and the like according to one or more aspects have beendescribed above based on the embodiments, but the present disclosure isnot limited to these embodiments. The one or more aspects may thusinclude forms achieved by making various modifications to the aboveembodiments that can be conceived by those skilled in the art, as wellforms achieved by combining structural components in differentembodiments, without materially departing from the spirit of the presentdisclosure.

INDUSTRIAL APPLICABILITY

The present disclosure is applicable to a three-dimensional dataencoding device and a three-dimensional data decoding device.

What is claimed is:
 1. A three-dimensional data encoding method using afirst encoding scheme and a second encoding scheme different from thefirst encoding scheme, the method comprising: performing transformation,the transformation being one of (i) transforming a first quantizationparameter to a first scale value and (ii) transforming the first scalevalue to the first quantization parameter, based on a first tableindicating a correspondence between a plurality of values of the firstquantization parameter and a plurality of values of the first scalevalue, the first table being shared between the first encoding schemeand the second encoding scheme; performing encoding including a firstquantization process to generate encoded attribute information, thefirst quantization process being a process of dividing, by the firstscale value, each of first coefficient values based on items ofattribute information of three-dimensional points included in pointcloud data; and generating a bitstream including the encoded attributeinformation and the first quantization parameter.
 2. Thethree-dimensional data encoding method according to claim 1, wherein theencoding including the first quantization process includes: performingleft bit shift on each of the items of attribute information to generateitems of shifted attribute information; and performing, on the items ofshifted attribute information, a transformation process using items ofgeometry information of the three-dimensional points to generate thefirst coefficient values, wherein the first scale value is a valuegenerated by multiplying a second scale value for quantization by acoefficient corresponding to the left bit shift, and the quantizationand right bit shift are performed by dividing each of the firstcoefficient values by the first scale value, the right bit shiftshifting a same number of bits as a number of bits shifted by the leftbit shift.
 3. The three-dimensional data encoding method according toclaim 2, wherein a first bit number is different from a second bitnumber, the first bit number being a number of bits shifted by each ofthe left bit shift and the right bit shift performed in the firstencoding scheme, the second bit number being a number of bits shifted byeach of the left bit shift and the right bit shift performed in thesecond encoding scheme, and in the first encoding scheme, the performingof the transformation includes: (i) applying the first table to thefirst scale value to determine the first quantization parameter; or (ii)applying the first table to the first quantization parameter todetermine the first scale value, and in the second encoding scheme, theperforming of the transformation includes: (i) performing bit shift onthe first scale value by a number of bits corresponding to a differencebetween the first bit number and the second bit number to obtain ashifted first scale value, and applying the first table to the shiftedfirst scale value to determine the first quantization parameter; or (ii)applying the first table to the first quantization parameter todetermine a third scale value, and performing bit shift on the thirdscale value by a number of bits corresponding to the difference betweenthe first bit number and the second bit number to generate the firstscale value.
 4. The three-dimensional data encoding method according toclaim 1, further comprising: performing transformation, thetransformation being one of (i) transforming a second quantizationparameter to a fourth scale value and (ii) transforming the fourth scalevalue to the second quantization parameter, based on a second tableindicating a correspondence between a plurality of values of the secondquantization parameter and a plurality of values of the fourth scalevalue, the second table being shared between the first encoding schemeand the second encoding scheme; and performing encoding including asecond quantization process to generate encoded geometry information,the second quantization process being a process of dividing, by thefourth scale value, each of second coefficient values based on items ofgeometry information of the three-dimensional points, wherein thebitstream further includes the encoded geometry information and thesecond quantization parameter.
 5. A three-dimensional data decodingmethod using a first decoding scheme and a second decoding schemedifferent from the first decoding scheme, the method comprising:obtaining encoded attribute information and a first quantizationparameter from a bitstream, the encoded attribute information beinggenerated by encoding items of attribute information ofthree-dimensional points included in point cloud data; transforming thefirst quantization parameter to a first scale value, based on a firsttable indicating a correspondence between a plurality of values of thefirst quantization parameter and a plurality of values of the firstscale value, the first table being shared between the first decodingscheme and the second decoding scheme; performing decoding including afirst inverse quantization process to decode the items of attributeinformation, the first inverse quantization process being a process ofmultiplying, by the first scale value, each of first quantizationcoefficients based on the encoded attribute information.
 6. Thethree-dimensional data decoding method according to claim 5, wherein thedecoding including the first inverse quantization process includes:performing the first inverse quantization process to generate firstcoefficient values from the first quantization coefficients; performinginverse transformation on the first coefficient values using items ofgeometry information of the three-dimensional points to generate itemsof shifted attribute information; and performing right bit shift on eachof the items of shifted attribute information to generate the items ofattribute information, the first scale value is a value generated bymultiplying a second scale value for inverse quantizations by acoefficient corresponding to the right bit shift, and left bit shift andthe inverse quantization are performed by multiplying each of the firstquantization coefficients by the first scale value, the left bit shiftshifting a same number of bits as a number of bits shifted by the rightbit shift.
 7. The three-dimensional data decoding method according toclaim 6, wherein a first bit number is different from a second bitnumber, the first bit number being a number of bits shifted by each ofthe right bit shift and the left bit shift performed in the firstdecoding scheme, the second bit number being a number of bits shifted byeach of the right bit shift and the left bit shift performed in thesecond decoding scheme, and in the first decoding scheme, thetransforming of the first quantization parameter to the first scalevalue includes applying the first table to the first quantizationparameter to determine the first scale value, and in the second decodingscheme, the transforming of the first quantization parameter to thefirst scale value includes applying the first table to the firstquantization parameter to determine a third scale value, and performingbit shift on the third scale value by a number of bits corresponding toa difference between the first bit number and the second bit number. 8.The three-dimensional data decoding method according to claim 5, furthercomprising: obtaining encoded geometry information and a secondquantization parameter from the bitstream, the encoded geometryinformation being generated by encoding items of geometry information ofthe three-dimensional points; transforming the second quantizationparameter to a fourth scale value, based on a second table indicating acorrespondence between a plurality of values of the second quantizationparameter and a plurality of values of the fourth scale value, thesecond table being shared between the first decoding scheme and thesecond decoding scheme; performing decoding including a second inversequantization process to obtain the items of geometry information, thesecond inverse quantization process being a process of multiplying, bythe fourth scale value, each of second quantization coefficients basedon the encoded geometry information.
 9. The three-dimensional datadecoding method according to claim 5, wherein when the firstquantization parameter is smaller than 4, the first quantizationparameter is assumed to be
 4. 10. A three-dimensional data encodingdevice using a first encoding scheme and a second encoding schemedifferent from the first encoding scheme, the device comprising: aprocessor; and memory, wherein using the memory, the processor: performstransformation, the transformation being one of (i) transforming a firstquantization parameter to a first scale value and (ii) transforming thefirst scale value to the first quantization parameter, based on a firsttable indicating a correspondence between a plurality of values of thefirst quantization parameter and a plurality of values of the firstscale value, the first table being shared between the first encodingscheme and the second encoding scheme; performs encoding including afirst quantization process to generate encoded attribute information,the first quantization process being a process of dividing, by the firstscale value, each of first coefficient values based on items ofattribute information of three-dimensional points included in pointcloud data; and generates a bitstream including the encoded attributeinformation and the first quantization parameter.
 11. Athree-dimensional data decoding device using a first decoding scheme anda second decoding scheme different from the first decoding scheme, thedevice comprising: a processor; and memory, wherein using the memory,the processor: obtains encoded attribute information and a firstquantization parameter from a bitstream, the encoded attributeinformation being generated by encoding items of attribute informationof three-dimensional points included in point cloud data; transforms thefirst quantization parameter to a first scale value, based on a firsttable indicating a correspondence between a plurality of values of thefirst quantization parameter and a plurality of values of the firstscale value, the first table being shared between the first decodingscheme and the second decoding scheme; performs decoding including afirst inverse quantization process to decode the items of attributeinformation, the first inverse quantization process being a process ofmultiplying, by the first scale value, each of first quantizationcoefficients based on the encoded attribute information.